Object tracking,an important technology in the field of image processing and computer vision,is used to continuously track a specific object or person in an image.This technology may be effective in identifying the sa...Object tracking,an important technology in the field of image processing and computer vision,is used to continuously track a specific object or person in an image.This technology may be effective in identifying the same person within one image,but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same.When tracking the same object using two or more images,there must be a way to determine that objects existing in different images are the same object.Therefore,this paper attempts to determine the same object present in different images using color information among the unique information of the object.Thus,this study proposes a multiple-object-tracking method using histogram stamp extraction in closed-circuit television applications.The proposed method determines the presence or absence of a target object in an image by comparing the similarity between the image containing the target object and other images.To this end,a unique color value of the target object is extracted based on its color distribution in the image using three methods:mean,mode,and interquartile range.The Top-N accuracy method is used to analyze the accuracy of each method,and the results show that the mean method had an accuracy of 93.5%(Top-2).Furthermore,the positive prediction value experimental results show that the accuracy of the mean method was 65.7%.As a result of the analysis,it is possible to detect and track the same object present in different images using the unique color of the object.Through the results,it is possible to track the same object that can minimize manpower without using personal information when detecting objects in different images.In the last response speed experiment,it was shown that when the mean was used,the color extraction of the object was possible in real time with 0.016954 s.Through this,it is possible to detect and track the same object in real time when using the proposed method.展开更多
In this paper,a statistical method called Generalized Equilibrium Feedback Analysis(GEFA)is used to investigate the responses of the North Pacific Storm Track(NPST)in the cold season to the multi-scale oceanic variati...In this paper,a statistical method called Generalized Equilibrium Feedback Analysis(GEFA)is used to investigate the responses of the North Pacific Storm Track(NPST)in the cold season to the multi-scale oceanic variations of the Kuroshio Extension(KE)system,including its large-scale variation,oceanic front meridional shift,and mesoscale eddy activity.Results show that in the cold season from the lower to the upper troposphere,the KE large-scale variation significantly weakens the storm track activity over the central North Pacific south of 30°N.The northward shift of the KE front significantly strengthens the storm track activity over the western and central North Pacific south of 40°N,resulting in a southward shift of the NPST.In contrast,the NPST response to KE mesoscale eddy activity is not so significant and relatively shallow,which only shows some significant positive signals near the dateline in the lower and middle troposphere.Furthermore,it is found that baroclinicity and baroclinic energy conversion play an important role in the formation of the NPST response to the KE multi-scale oceanic variations.展开更多
At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the per...At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.展开更多
The field of object tracking has recently made significant progress.Particularly,the performance results in both deep learning and correlation filters,based trackers achieved effective tracking performance.Moreover,th...The field of object tracking has recently made significant progress.Particularly,the performance results in both deep learning and correlation filters,based trackers achieved effective tracking performance.Moreover,there are still some difficulties with object tracking for example illumination and deformation(DEF).The precision and accuracy of tracking algorithms suffer from the effects of such occurrences.For this situation,finding a solution is important.This research proposes a new tracking algorithm to handle this problem.The features are extracted by using Modified LeNet-5,and the precision and accuracy are improved by developing the Real-Time Cross-modality Correlation Filtering method(RCCF).In Modified LeNet-5,the visual tracking performance is improved by adjusting the number and size of the convolution kernels in the pooling and convolution layers.The high-level,middle-level,and handcraft features are extracted from the modified LeNet-5 network.The handcraft features are used to determine the specific location of the target because the handcraft features contain more spatial information regarding the visual object.The LeNet features are more suitable for a target appearance change in object tracking.Extensive experiments were conducted by the Object Tracking Benchmarking(OTB)databases like OTB50 and OTB100.The experimental results reveal that the proposed tracker outperforms other state-of-the-art trackers under different problems.The experimental simulation is carried out in python.The overall success rate and precision of the proposed algorithm are 93.8%and 92.5%.The average running frame rate reaches 42 frames per second,which can meet the real-time requirements.展开更多
Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific...Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific proposals is proposed to optimize the mathematical model of the correlation filter(CF).Firstly,according to the consistency of the changes between the object frames and the filter frames,the mask matrix is introduced into the objective function of the filter,so as to extract the spatio-temporal information of the object with background awareness.Secondly,the object function of multi-feature fusion is constructed for the object location,which is optimized by the Lagrange method and solved by closed iteration.In the process of filter optimization,the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final track-ing results.Finally,when the tracking results fluctuate,the boundary suppres-sion factor is introduced into the instance-specific proposals to reduce the risk of model drift effectively.The accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks,the object tracking benchmark 2015(OTB2015)and the temple color 128(TC-128).Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is effective.The distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark,which are better than the results of other competing algorithms.The results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.展开更多
The amount of needed control messages in wireless sensor networks(WSN)is affected by the storage strategy of detected events.Because broadcasting superfluous control messages consumes excess energy,the network lifespa...The amount of needed control messages in wireless sensor networks(WSN)is affected by the storage strategy of detected events.Because broadcasting superfluous control messages consumes excess energy,the network lifespan can be extended if the quantity of control messages is decreased.In this study,an optimized storage technique having low control overhead for tracking the objects in WSN is introduced.The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List(OBLL).Whenever an object passes over the sensor area,the recognizing sensor can immediately produce an OBLL along the object’s route.To retrieve the entire information,the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list structures.Performance evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.展开更多
This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,th...This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,that involves a frame of reference in spatial domain to localize and/or track any object.Thefield of multiple object tracking has seen a lot of research,but researchers have considered the background as redundant.However,in object tracking,the back-ground plays a vital role and leads to definite improvement in the overall process of tracking.In the present work an algorithm is proposed for the multiple object tracking through background learning.The learning framework is based on graph embedding approach for localizing multiple objects.The graph utilizes the inher-ent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple objects.The proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation measures.It is observed that our proposed algorithm gives better performance.展开更多
This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influ...This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis.展开更多
On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detect...On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detection paradigm,a commonly utilized approach,connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the simila-rities of the appearance or the motion between them.For an efficient detection and tracking of the numerous objects in a complex environment,a Pearson Simi-larity-centred Kuhn-Munkres(PS-KM)algorithm was proposed in the present study.In this light,the input videos were,initially,gathered from the MOT dataset and converted into frames.The background subtraction occurred whichfiltered the inappropriate data concerning the frames after the frame conversion stage.Then,the extraction of features from the frames was executed.Afterwards,the higher dimensional features were transformed into lower-dimensional features,and feature reduction process was performed with the aid of Information Gain-centred Singular Value Decomposition(IG-SVD).Next,using the Modified Recurrent Neural Network(MRNN)method,classification was executed which identified the categories of the objects additionally.The PS-KM algorithm identi-fied that the recognized objects were tracked.Finally,the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97%accuracy with a low false positive rate(FPR)of 2.3%.It was also proved that the present techniques viz.RNN,CNN,and KNN,were effective with regard to the existing models.展开更多
A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation (MTREC) was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of r...A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation (MTREC) was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation (TREC) technique with a large "box" to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small "box" to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies: the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.展开更多
The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. Typically, refl...The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. Typically, reflectivity data from multiple radars are objectively analyzed and mosaiced onto a regional 3-D Cartesian grid prior to being assimilated into the models. One multi-radar observations is the synchronization of all of the scientific issues associated with the mosaic of the observations. Since radar data is usually rapidly updated (-every 5-10 min), it is common in current multi-radar mosaic techniques to combine multiple radar' observations within a time window by assunfing that the storms are steady within the window. The assumption holds well for slow evolving precipitation systems, but for fast evolving convective storms, this assumption may be violated and the mosaic of radar observations at different times may result in inaccurate storm structure depictions. This study investigates the impact of synchronization on storm structures in multiple radar data analyses using a multi-scale storm tracking algorithm.展开更多
In this paper we address the problem of tracking human poses in multiple perspective scales in 2D monocular images/videos. In most state-of-the-art 2D tracking approaches, the issue of scale variation is rarely discus...In this paper we address the problem of tracking human poses in multiple perspective scales in 2D monocular images/videos. In most state-of-the-art 2D tracking approaches, the issue of scale variation is rarely discussed. However in reality, videos often contain human motion with dynamically changed scales. In this paper we propose a tracking framework that can deal with this problem. A scale checking and adjusting algorithm is proposed to automatically adjust the perspective scales during the tracking process. Two metrics are proposed for detecting and adjusting the scale change. One metric is from the height value of the tracked target, which is suitable for some sequences where the tracked target is upright and with no limbs stretching. The other metric employed in this algorithm is more generic, which is invariant to motion types. It is the ratio between the pixel counts of the target silhouette and the detected bounding boxes of the target body. The proposed algorithm is tested on the publicly available datasets (HumanEva). The experimental results show that our method demonstrated higher accuracy and efficiency compared to state-of-the-art approaches.展开更多
Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challen...Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challenges and the large variety of applications.This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it.It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile.The goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of interest.The suggested system is divided into six stages.The photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all co-registered.The ROI,or road and its objects,are retrieved using the GrabCut method in the second phase.The third phase entails data preparation.The segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological procedures.The YOLOv3 algorithm is used in the fourth step to find any automobiles in the photos.Following that,the Kalman filter and centroid tracking are used to perform the tracking of the detected cars.The Lucas-Kanade method is then used to perform the trajectory analysis on the vehicles.The suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)dataset.For detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.展开更多
Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.Howev...Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.However,most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs.To meet the tracking precision and real-time requirements,this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL.Specifically,the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network,then performs correlationmatching to obtain the candidate regionwith high similarity.To improve the matching effect of template and search features,this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection.An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions.In addition,a target localization module is designed to improve target location accuracy.Compared with other advanced trackers,experiments on two public benchmarks,which are UAV123@10fps and UAV20L fromthe unmanned air vehicle123(UAV123)dataset,show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.展开更多
This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework i...This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow (underwater acoustic images) is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.展开更多
A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transf...A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform (RDWT) based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.展开更多
Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-u...Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 × 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.展开更多
An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method...An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal "visual" object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022R1F1A1068828).
文摘Object tracking,an important technology in the field of image processing and computer vision,is used to continuously track a specific object or person in an image.This technology may be effective in identifying the same person within one image,but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same.When tracking the same object using two or more images,there must be a way to determine that objects existing in different images are the same object.Therefore,this paper attempts to determine the same object present in different images using color information among the unique information of the object.Thus,this study proposes a multiple-object-tracking method using histogram stamp extraction in closed-circuit television applications.The proposed method determines the presence or absence of a target object in an image by comparing the similarity between the image containing the target object and other images.To this end,a unique color value of the target object is extracted based on its color distribution in the image using three methods:mean,mode,and interquartile range.The Top-N accuracy method is used to analyze the accuracy of each method,and the results show that the mean method had an accuracy of 93.5%(Top-2).Furthermore,the positive prediction value experimental results show that the accuracy of the mean method was 65.7%.As a result of the analysis,it is possible to detect and track the same object present in different images using the unique color of the object.Through the results,it is possible to track the same object that can minimize manpower without using personal information when detecting objects in different images.In the last response speed experiment,it was shown that when the mean was used,the color extraction of the object was possible in real time with 0.016954 s.Through this,it is possible to detect and track the same object in real time when using the proposed method.
基金jointly supported by the National Natural Science Foundation of China (Grant Nos. 42105066, 42088101, 41975066)supported by the China Postdoctoral Science Foundation (2021M701754)+1 种基金the Postdoctoral Research Funding of Jiangsu Province (2021K052A)the Research Project of the National University of Defense Technology (ZK20-45)
文摘In this paper,a statistical method called Generalized Equilibrium Feedback Analysis(GEFA)is used to investigate the responses of the North Pacific Storm Track(NPST)in the cold season to the multi-scale oceanic variations of the Kuroshio Extension(KE)system,including its large-scale variation,oceanic front meridional shift,and mesoscale eddy activity.Results show that in the cold season from the lower to the upper troposphere,the KE large-scale variation significantly weakens the storm track activity over the central North Pacific south of 30°N.The northward shift of the KE front significantly strengthens the storm track activity over the western and central North Pacific south of 40°N,resulting in a southward shift of the NPST.In contrast,the NPST response to KE mesoscale eddy activity is not so significant and relatively shallow,which only shows some significant positive signals near the dateline in the lower and middle troposphere.Furthermore,it is found that baroclinicity and baroclinic energy conversion play an important role in the formation of the NPST response to the KE multi-scale oceanic variations.
文摘At present days,object detection and tracking concepts have gained more importance among researchers and business people.Presently,deep learning(DL)approaches have been used for object tracking as it increases the perfor-mance and speed of the tracking process.This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Anno-tation with ResNet based Faster regional convolutional neural network(R-CNN)named(AIA-FRCNN)model.The AIA-RFRCNN method performs image anno-tation using a Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR)called DCF-CSRT model.The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker,which involves region proposal network(RPN)and Fast R-CNN.The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects.The RPN is a trained model used for the generation of the high-quality region proposals,which are utilized by Fast R-CNN for detection process.Besides,Residual Network(ResNet 101)model is used as a shared convolutional neural network(CNN)for the generation of feature maps.The performance of the ResNet 101 model is further improved by the use of Adam optimizer,which tunes the hyperparameters namely learning rate,batch size,momentum,and weight decay.Finally,softmax layer is applied to classify the images.The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place.The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.
文摘The field of object tracking has recently made significant progress.Particularly,the performance results in both deep learning and correlation filters,based trackers achieved effective tracking performance.Moreover,there are still some difficulties with object tracking for example illumination and deformation(DEF).The precision and accuracy of tracking algorithms suffer from the effects of such occurrences.For this situation,finding a solution is important.This research proposes a new tracking algorithm to handle this problem.The features are extracted by using Modified LeNet-5,and the precision and accuracy are improved by developing the Real-Time Cross-modality Correlation Filtering method(RCCF).In Modified LeNet-5,the visual tracking performance is improved by adjusting the number and size of the convolution kernels in the pooling and convolution layers.The high-level,middle-level,and handcraft features are extracted from the modified LeNet-5 network.The handcraft features are used to determine the specific location of the target because the handcraft features contain more spatial information regarding the visual object.The LeNet features are more suitable for a target appearance change in object tracking.Extensive experiments were conducted by the Object Tracking Benchmarking(OTB)databases like OTB50 and OTB100.The experimental results reveal that the proposed tracker outperforms other state-of-the-art trackers under different problems.The experimental simulation is carried out in python.The overall success rate and precision of the proposed algorithm are 93.8%and 92.5%.The average running frame rate reaches 42 frames per second,which can meet the real-time requirements.
基金funded by the Basic Science Major Foundation(Natural Science)of the Jiangsu Higher Education Institutions of China(Grant:22KJA520012)the Xuzhou Science and Technology Plan Project(Grant:KC21303,KC22305)the sixth“333 project”of Jiangsu Province.
文摘Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion,a tracking algorithm based on multi-time-space perception and instance-specific proposals is proposed to optimize the mathematical model of the correlation filter(CF).Firstly,according to the consistency of the changes between the object frames and the filter frames,the mask matrix is introduced into the objective function of the filter,so as to extract the spatio-temporal information of the object with background awareness.Secondly,the object function of multi-feature fusion is constructed for the object location,which is optimized by the Lagrange method and solved by closed iteration.In the process of filter optimization,the constraints term of time-space perception is designed to enhance the learning ability of the CF to optimize the final track-ing results.Finally,when the tracking results fluctuate,the boundary suppres-sion factor is introduced into the instance-specific proposals to reduce the risk of model drift effectively.The accuracy and success rate of the proposed algorithm are verified by simulation analysis on two popular benchmarks,the object tracking benchmark 2015(OTB2015)and the temple color 128(TC-128).Extensive experimental results illustrate that the optimized appearance model of the proposed algorithm is effective.The distance precision rate and overlap success rate of the proposed algorithm are 0.756 and 0.656 on the OTB2015 benchmark,which are better than the results of other competing algorithms.The results of this study can solve the problem of real-time object tracking in the real traffic environment and provide a specific reference for the detection of traffic abnormalities.
文摘The amount of needed control messages in wireless sensor networks(WSN)is affected by the storage strategy of detected events.Because broadcasting superfluous control messages consumes excess energy,the network lifespan can be extended if the quantity of control messages is decreased.In this study,an optimized storage technique having low control overhead for tracking the objects in WSN is introduced.The basic concept is to retain observed events in internal memory and preserve the relationship between sensed information and sensor nodes using a novel inexpensive data structure entitled Ordered Binary Linked List(OBLL).Whenever an object passes over the sensor area,the recognizing sensor can immediately produce an OBLL along the object’s route.To retrieve the entire information,the OBLL can be traversed with logarithmic complexity which is much less than the traversing complexity of existing linked list structures.Performance evaluation and simulations were carried out to ensure that the suggested technique minimizes the number of messages and thus saving energy and extending the network life.
文摘This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,that involves a frame of reference in spatial domain to localize and/or track any object.Thefield of multiple object tracking has seen a lot of research,but researchers have considered the background as redundant.However,in object tracking,the back-ground plays a vital role and leads to definite improvement in the overall process of tracking.In the present work an algorithm is proposed for the multiple object tracking through background learning.The learning framework is based on graph embedding approach for localizing multiple objects.The graph utilizes the inher-ent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple objects.The proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation measures.It is observed that our proposed algorithm gives better performance.
文摘This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis.
文摘On grounds of the advent of real-time applications,like autonomous driving,visual surveillance,and sports analysis,there is an augmenting focus of attention towards Multiple-Object Tracking(MOT).The tracking-by-detection paradigm,a commonly utilized approach,connects the existing recognition hypotheses to the formerly assessed object trajectories by comparing the simila-rities of the appearance or the motion between them.For an efficient detection and tracking of the numerous objects in a complex environment,a Pearson Simi-larity-centred Kuhn-Munkres(PS-KM)algorithm was proposed in the present study.In this light,the input videos were,initially,gathered from the MOT dataset and converted into frames.The background subtraction occurred whichfiltered the inappropriate data concerning the frames after the frame conversion stage.Then,the extraction of features from the frames was executed.Afterwards,the higher dimensional features were transformed into lower-dimensional features,and feature reduction process was performed with the aid of Information Gain-centred Singular Value Decomposition(IG-SVD).Next,using the Modified Recurrent Neural Network(MRNN)method,classification was executed which identified the categories of the objects additionally.The PS-KM algorithm identi-fied that the recognized objects were tracked.Finally,the experimental outcomes exhibited that numerous targets were precisely tracked by the proposed system with 97%accuracy with a low false positive rate(FPR)of 2.3%.It was also proved that the present techniques viz.RNN,CNN,and KNN,were effective with regard to the existing models.
基金This study was supported by the Special Fund for Basic Research and Operation of Chinese Academy of Meteorological Science:Development on quantitative precipitation forecasts for 0-6 h lead times by blending radar-based extrapolation and GRAPES-meso,Observation and retrieval methods of micro-physics,the National Natural Science Foundation of China
文摘A new radar echo tracking algorithm known as multi-scale tracking radar echoes by cross-correlation (MTREC) was developed in this study to analyze movements of radar echoes at different spatial scales. Movement of radar echoes, particularly associated with convective storms, exhibits different characteristics at various spatial scales as a result of complex interactions among meteorological systems leading to the formation of convective storms. For the null echo region, the usual correlation technique produces zero or a very small magnitude of motion vectors. To mitigate these constraints, MTREC uses the tracking radar echoes by correlation (TREC) technique with a large "box" to determine the systematic movement driven by steering wind, and MTREC applies the TREC technique with a small "box" to estimate small-scale internal motion vectors. Eventually, the MTREC vectors are obtained by synthesizing the systematic motion and the small-scale internal motion. Performance of the MTREC technique was compared with TREC technique using case studies: the Khanun typhoon on 11 September 2005 observed by Wenzhou radar and a squall-line system on 23 June 2011 detected by Beijing radar. The results demonstrate that more spatially smoothed and continuous vector fields can be generated by the MTREC technique, which leads to improvements in tracking the entire radar reflectivity pattern. The new multi-scMe tracking scheme was applied to study its impact on the performance of quantitative precipitation nowcasting. The location and intensity of heavy precipitation at a 1-h lead time was more consistent with quantitative precipitation estimates using radar and rain gauges.
基金Major funding for this research was provided under the United States Federal Aviation Administration (FAA) Aviation Weather Research Program Advanced Weather Radar Technologies Prod-uct Development Team Memorandum Of Understanding(MOU)partial funding was provided under NOAA-University of Oklahoma Cooperative Agreement Grant No. NA17RJ1227, U.S. Department of Commerce
文摘The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. Typically, reflectivity data from multiple radars are objectively analyzed and mosaiced onto a regional 3-D Cartesian grid prior to being assimilated into the models. One multi-radar observations is the synchronization of all of the scientific issues associated with the mosaic of the observations. Since radar data is usually rapidly updated (-every 5-10 min), it is common in current multi-radar mosaic techniques to combine multiple radar' observations within a time window by assunfing that the storms are steady within the window. The assumption holds well for slow evolving precipitation systems, but for fast evolving convective storms, this assumption may be violated and the mosaic of radar observations at different times may result in inaccurate storm structure depictions. This study investigates the impact of synchronization on storm structures in multiple radar data analyses using a multi-scale storm tracking algorithm.
文摘In this paper we address the problem of tracking human poses in multiple perspective scales in 2D monocular images/videos. In most state-of-the-art 2D tracking approaches, the issue of scale variation is rarely discussed. However in reality, videos often contain human motion with dynamically changed scales. In this paper we propose a tracking framework that can deal with this problem. A scale checking and adjusting algorithm is proposed to automatically adjust the perspective scales during the tracking process. Two metrics are proposed for detecting and adjusting the scale change. One metric is from the height value of the tracked target, which is suitable for some sequences where the tracked target is upright and with no limbs stretching. The other metric employed in this algorithm is more generic, which is invariant to motion types. It is the ratio between the pixel counts of the target silhouette and the detected bounding boxes of the target body. The proposed algorithm is tested on the publicly available datasets (HumanEva). The experimental results show that our method demonstrated higher accuracy and efficiency compared to state-of-the-art approaches.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2023-RS-2022-00156326)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation).
文摘Unmanned aerial vehicles(UAVs)can be used to monitor traffic in a variety of settings,including security,traffic surveillance,and traffic control.Numerous academics have been drawn to this topic because of the challenges and the large variety of applications.This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it.It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile.The goal of this study is to develop a method that first extracts the region of interest(ROI),then finds and tracks the items of interest.The suggested system is divided into six stages.The photos from the obtained dataset are appropriately georeferenced to their actual locations in the first phase,after which they are all co-registered.The ROI,or road and its objects,are retrieved using the GrabCut method in the second phase.The third phase entails data preparation.The segmented images’noise is eliminated using Gaussian blur,after which the images are changed to grayscale and forwarded to the following stage for additional morphological procedures.The YOLOv3 algorithm is used in the fourth step to find any automobiles in the photos.Following that,the Kalman filter and centroid tracking are used to perform the tracking of the detected cars.The Lucas-Kanade method is then used to perform the trajectory analysis on the vehicles.The suggested model is put to the test and assessed using the Vehicle Aerial Imaging from Drone(VAID)dataset.For detection and tracking,the model was able to attain accuracy levels of 96.7%and 91.6%,respectively.
基金funded by the National Natural Science Foundation of China(Grant No.52072408),author Y.C.
文摘Onboard visual object tracking in unmanned aerial vehicles(UAVs)has attractedmuch interest due to its versatility.Meanwhile,due to high precision,Siamese networks are becoming hot spots in visual object tracking.However,most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs.To meet the tracking precision and real-time requirements,this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL.Specifically,the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network,then performs correlationmatching to obtain the candidate regionwith high similarity.To improve the matching effect of template and search features,this paper designs a dense pixel-level feature fusion module to enhance the matching ability by pixel-wise correlation and enrich the feature diversity by dense connection.An attention module composed of self-attention and channel attention is introduced to learn global context information and selectively emphasize the target feature region in the spatial and channel dimensions.In addition,a target localization module is designed to improve target location accuracy.Compared with other advanced trackers,experiments on two public benchmarks,which are UAV123@10fps and UAV20L fromthe unmanned air vehicle123(UAV123)dataset,show that SiamDPL can achieve superior performance and low complexity with a running speed of 100.1 fps on NVIDIA TITAN RTX.
基金supported by the National Natural Science Foundation of China(Grant No.51009040)Heilongjiang Postdoctoral Fund(Grant No.LBH-Z11205)+1 种基金the National High Technology Research and Development Program of China(863 Program,Grant No.2011AA09A106)the China Postdoctoral Science Foundation(Grant No.2012M510928)
文摘This paper describes a new framework for object detection and tracking of AUV including underwater acoustic data interpolation, underwater acoustic images segmentation and underwater objects tracking. This framework is applied to the design of vision-based method for AUV based on the forward looking sonar sensor. First, the real-time data flow (underwater acoustic images) is pre-processed to form the whole underwater acoustic image, and the relevant position information of objects is extracted and determined. An improved method of double threshold segmentation is proposed to resolve the problem that the threshold cannot be adjusted adaptively in the traditional method. Second, a representation of region information is created in light of the Gaussian particle filter. The weighted integration strategy combining the area and invariant moment is proposed to perfect the weight of particles and to enhance the tracking robustness. Results obtained on the real acoustic vision platform of AUV during sea trials are displayed and discussed. They show that the proposed method can detect and track the moving objects underwater online, and it is effective and robust.
文摘A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform (RDWT) based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.
基金supported by National Basic Research Program of China (973 Program) (No. 2006CB300407)National Natural Science Foundation of China (No. 50775017)
文摘Inspired by human behaviors, a robot object tracking model is proposed on the basis of visual attention mechanism, which is fit for the theory of topological perception. The model integrates the image-driven, bottom-up attention and the object-driven, top-down attention, whereas the previous attention model has mostly focused on either the bottom-up or top-down attention. By the bottom-up component, the whole scene is segmented into the ground region and the salient regions. Guided by top-down strategy which is achieved by a topological graph, the object regions are separated from the salient regions. The salient regions except the object regions are the barrier regions. In order to estimate the model, a mobile robot platform is developed, on which some experiments are implemented. The experimental results indicate that processing an image with a resolution of 752 × 480 pixels takes less than 200 ms and the object regions are unabridged. The analysis obtained by comparing the proposed model with the existing model demonstrates that the proposed model has some advantages in robot object tracking in terms of speed and efficiency.
基金supported by the National Natural Science Foundation of China(60835004 60775047+2 种基金 60872130)the National High Technology Research and Development Program of China(863 Program)(2007AA04Z244 2008AA04Z214)
文摘An object model-based tracking method is useful for tracking multiple objects, but the main difficulties are modeling objects reliably and tracking objects via models in successive frames. An effective tracking method using the object models is proposed to track multiple objects in a real-time visual surveillance system. Firstly, for detecting objects, an adaptive kernel density estimation method is utilized, which uses an adaptive bandwidth and features combining colour and gradient. Secondly, some models of objects are built for describing motion, shape and colour features. Then, a matching matrix is formed to analyze tracking situations. If objects are tracked under occlusions, the optimal "visual" object is found to represent the occluded object, and the posterior probability of pixel is used to determine which pixel is utilized for updating object models. Extensive experiments show that this method improves the accuracy and validity of tracking objects even under occlusions and is used in real-time visual surveillance systems.