Learning Dual-Domain Calibration and Distance-Driven Correlation Filter:A Probabilistic Perspective for UAV Tracking

Unmanned Aerial Vehicle(UAV)tracking has been possible because of the growth of intelligent information technology in smart cities,making it simple to gather data at any time by dynamically monitoring events,people,the environment,and other aspects in the city.The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations.But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms.In light of the aforementioned issues,this work suggests a dual-domain Jensen-Shannon divergence correlation filter(DJSCF)model address the probability-based distance measuring issue in the event of filter degradation.The two-domain weighting matrix and JS divergence constraint are combined to lessen the impact of sample imbalance and distortion.Two new tracking models that are based on the perspectives of the actual probability filter distribution and observation probability filter distribution are proposed to translate the statistical distance in the online tracking model into response fitting.The model is roughly transformed into a linear equality constraint issue in the iterative solution,which is then solved by the alternate direction multiplier method(ADMM).The usefulness and superiority of the suggested strategy have been shown by a vast number of experimental findings.

A Method for Incipient Fault Diagnosis of Roller Bearings Based on the Wavelet Transform Correlation Filter and Hilbert Transform

Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.

An Adaptive Padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

In recent visual tracking research,correlation filter(CF)based trackers become popular because of their high speed and considerable accuracy.Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter.However,the related studies are insufficient.By exploring the potential of trackers in these two aspects,a novel adaptive padding correlation filter(APCF)with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework.In the tracker,three feature groups are fused by use of the weighted sum of the normalized response maps,to alleviate the risk of drift caused by the extreme change of single feature.Moreover,to improve the adaptive ability of padding for the filter training of different object shapes,the best padding is selected from the preset pool according to tracking precision over the whole video,where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames.The sequence features include three traditional features and eight newly constructed features.Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.

Visual tracking for underwater sea cucumber via correlation filters

One of the essential techniques for using underwater robots to fish sea cucumbers is that the robots must track sea cucumbers using computer vision technology.Tracking underwater targets is a challenging task due to suspension,water absorption,and light scattering.This study proposed a simple but effective algorithm for sea cucumber tracking based on Kernelized Correlation Filters(KCF)framework.This method tracked the head and tail of the sea cucumber respectively and calculated the scale change according to the distance between the head and tail.The KCF method was improved on three strategies.First of all,the target was searched at the predicted position to improve accuracy.Secondly,an adaptive learning rate updating method based on the detection score of each frame was proposed.Finally,the adaptive size of the histogram of the oriented gradient(HOG)feature was used to balance the accuracy and efficiency.Experimental results showed that the algorithm had good tracking performance.

Intelligent droplet tracking with correlation filters for digital microfluidics

Tracking the movement of droplets in digital microfluidics is essential to improve its control stability and obtain dynamic information for its applications such as point-of-care testing,environment monitoring and chemical synthesis.Herein,an intelligent,accurate and fast droplet tracking method based on machine vision is developed for applications of digital microfluidics.To continuously recognize the transparent droplets in real-time and avoid the interferes from background patterns or inhomogeneous illumination,we introduced the correlation filter tracker,enabling online learning of the multi-features of the droplets in Fourier domain.Results show the proposed droplet tracking method could accurately locate the droplets.We also demonstrated the capacity of the proposed method for estimation of the droplet velocity as faster as 20 mm/s,and its application in online monitoring the Griess reaction for both colorimetric assay of nitrite and study of reaction kinetics.

Asymmetric discriminative correlation filters for visual tracking

Discriminative correlation filters(DCF)are efficient in visual tracking and have advanced the field significantly.However,the symmetry of correlation(or convolution)operator results in computational problems and does harm to the generalized translation equivariance.The former problem has been approached in many ways,whereas the latter one has not been well recognized.In this paper,we analyze the problems with the symmetry of circular convolution and propose an asymmetric one,which as a generalization of the former has a weak generalized translation equivariance property.With this operator,we propose a tracker called the asymmetric discriminative correlation filter(ADCF),which is more sensitive to translations of targets.Its asymmetry allows the filter and the samples to have different sizes.This flexibility makes the computational complexity of ADCF more controllable in the sense that the number of filter parameters will not grow with the sample size.Moreover,the normal matrix of ADCF is a block matrix with each block being a two-level block Toeplitz matrix.With this well-structured normal matrix,we design an algorithm for multiplying an N×N two-level block Toeplitz matrix by a vector with time complexity O(N log N)and space complexity O(N),instead of O(N^2).Unlike DCF-based trackers,introducing spatial or temporal regularization does not increase the essential computational complexity of ADCF.Comparative experiments are performed on a synthetic dataset and four benchmarks,including OTB-2013,OTB-2015,VOT-2016,and Temple-Color,and the results show that our method achieves state-of-the-art visual tracking performance.

Application of wavelet scale correlation filtering and its improved algorithm in signal processing with a spark sound source

It is seriously interfered by ship noise when analyzing and extracting broadband spark sound source signal. In the energy concentrated domain which is below 5 kHz, the traditional scale correlation filtering algorithm, which is based on adjacent-scale correlation, has limited anti-interference ability due to the low signal-to-noise ratio （SNR） and similar Lipschitz exponent characteristic of each other. However, because different frequency bands of the broadband electric spark signal have different noise interferences, the filtering algorithm based on adjacent-scale correlation is adapted to high SNR and small-scale high-frequency wavelet coefficients filtering; the filtering algorithm based on cross-scale correlation is adapted to low SNR and large-scale low-frequency wavelet coefficients filtering, and the threshold coefficient selection method had been corrected in the algorithm. It is shown that the filtering algorithm has a good filtering effect and extracts the broadband spark sound source signal effectively; it is applicable to broadband underwater acoustic signM processing in the presence of narrow-band strong interference background noise.

Object Tracking Algorithm Based on Multi-Time-Space Perception and Instance-Specific Proposals

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.

Visual Object Tracking Based on Modified LeNet-5 and RCCF

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.

Noise Controlling Methods in Frequency Selection Detecting Radar

Based on time correlation characteristic, width correlation characteristic and frequency correlation characteristic of detecting pulses, several methods are introduced to control random or periodic noise whose width is narrower than 1 ms or wider than 3 ms in Frequency Selection Detecting Radar System. The software flow chart and the results of the experiment are also given.

The TBD method for dim targets based on multi-level crossover and matching operator

In order to improve the tracking performance in this paper following TBD(Track before Detection) framework multi-level crossover and matching operator is presented.In data association stage the greedy principle is adopted to handle time complexity in DPA and at the same time crossover mathing operator is given to construct candidate trajectory.In addition the corresponding strategy is introduced in preprocessing and postprocessing to remove clutter and suppress false alarm rate.By the experimental comparison and analysis it can be found that the method is more perfer to strengthen the tracking performance of targets with SNR < 2.0 dB.

Robust Visual Tracking with Hierarchical Deep Features Weighted Fusion

To solve the problem of low robustness of trackers under significant appearance changes in complex background,a novel moving target tracking method based on hierarchical deep features weighted fusion and correlation filter is proposed.Firstly,multi-layer features are extracted by a deep model pre-trained on massive object recognition datasets.The linearly separable features of Relu3-1,Relu4-1 and Relu5-4 layers from VGG-Net-19 are especially suitable for target tracking.Then,correlation filters over hierarchical convolutional features are learned to generate their correlation response maps.Finally,a novel approach of weight adjustment is presented to fuse response maps.The maximum value of the final response map is just the location of the target.Extensive experiments on the object tracking benchmark datasets demonstrate the high robustness and recognition precision compared with several state-of-the-art trackers under the different conditions.

Anti-occlusion pedestrian tracking algorithm based on location prediction and deep feature rematch

Aiming to the problem of pedestrian tracking with frequent or long-term occlusion in complex scenes,an anti-occlusion pedestrian tracking algorithm based on location prediction and deep feature rematch is proposed.Firstly,the occlusion judgment is realized by extracting and utilizing deep feature of pedestrian’s appearance,and then the scale adaptive kernelized correlation filter is introduced to implement pedestrian tracking without occlusion.Secondly,Karman filter is introduced to predict the location of occluded pedestrian position.Finally,the deep feature is used to the rematch of pedestrian in the reappearance process.Simulation experiment and analysis show that the proposed algorithm can effectively detect and rematch pedestrian under the condition of frequent or long-term occlusion.

Noise level estimation method with application to EMD-based signal denoising

This paper proposes a new signal noise level estimation approach by local regions. The estimated noise variance is applied as the threshold for an improved empirical mode decomposition（EMD） based signal denoising method. The proposed estimation method can effectively extract the candidate regions for the noise level estimation by measuring the correlation coefficient between noisy signal and a Gaussian filtered signal. For the improved EMD based method, the situation of decomposed intrinsic mode function（IMFs） which contains noise and signal simultaneously are taken into account. Experimental results from two simulated signals and an X-ray pulsar signal demonstrate that the proposed method can achieve better performance than the conventional EMD and wavelet transform（WT） based denoising methods.

Cell Search Algorithm in Beyond 3G Systems

The downlink frame structure for beyond 3G mobile communication systems is presented. Beyond 3G systems utilize the OFDM technique. However, a problem encountered in OFDM is that channel dispersion destroys orthogonality between carriers, caushag inter-symbol interference. It is also sensitive to high peak to mean power ratio （PAPR）. Therefore it spends much time on obtaining frequency, time, and frame synchronization. This paper proposes to add a frame synchronization channel in the time domain to overcome the shortcoming of OFDM. As transmitter diversity improves the system performance, beyond 3G systems employ space-time block coded （STBC）. Fast cell search algorithm including slot synchronization, frame synchronization and cell ID identification is then discussed, which is based on the frame synchronization channel in transmitter diversity systems. Detection and false alarm probabilities in AWGN and Rayleigh channels are analyzed, and the mean acquisition time is obtained. Computer simulations are conducted to evaluate the performance of the cell search algorithm under different channel conditions.

3D image reconstruction with controllable spatial filtering based on correlation of multiple periodic functions in computational integral imaging

We propose a novel method of slice image reconstruction with controllable spatial filtering by using the correlation of periodic delta-function arrays （PDFAs） with elemental images in computational integral imaging. The multiple PDFAs, whose spatial periods correspond to object＇s depths with the elemental image array （EIA）, can generate a set of spatially filtered EIAs for multiple object depths compared with the conventional method for the depth of a single object. We analyze a controllable spatial filtering effect by the proposed method. To show the feasibility of the proposed method, we carry out preliminary experiments for multiple objects and present the results.

Robust template feature matching method using motion-constrained DCF designed for visual navigation in asteroid landing

A robust and eficient feature matching method is necessary for visual navigation in asteroid-landing missions.Based on the visual navigation framework and motion characteristics of asteroids,a robust and efficient template feature matching method is proposed to adapt to feature distortion and scale change cases for visual navigation of asteroids.The proposed method is primarily based on a motion-constrained discriminative correlation filter(DCF).The prior information provided by the motion constraints between sequence images is used to provide a predicted search region for template feature matching.Additionally,some specific template feature samples are generated using the motion constraints for correlation filter learning,which is beneficial for training a scale and feature distortion adaptive correlation filter for accurate feature matching.Moreover,average peak-to-correlation energy(APCE)and jointly consistent measurements(JCMs)were used to eliminate false matching.Images captured by the Touch And Go Camera System(TAGCAMS)of the Bennu asteroid were used to evaluate the performance of the proposed method.In particular,both the robustness and accuracy of region matching and template center matching are evaluated.The qualitative and quantitative results illustrate the advancement of the proposed method in adapting to feature distortions and large-scale changes during spacecraft landing.

Pig target tracking algorithm based on multi-channel color feature fusion

In the process of tracking the target of the pig,with the change of the size of the tracking target in the video image,the estimated tracking target scale cannot be adaptively updated in real-time,resulting in the low accuracy of the tracking target.In this study,a multi-channel color feature adaptive fusion algorithm was proposed,and the target scale of the pig was updated in real-time by utilizing the contour information of the target pig.Experiments show that the proposed algorithm had a distance precision of 89.7%and an overlap precision of 87.5%,and the average running speed of this algorithm was 50.1 fps.The robustness of the proposed algorithm in tracking target deformation and scale variation were significantly improved,which satisfies the accuracy and real-time requirements of pig target tracking.

Real-time adaptive particle image velocimetry for accurate unsteady flow field measurements

Almost all conventional open-loop particle image velocimetry(PIV) methods employ fixed-interval-time optical imaging technology and the time-consuming cross-correlation-based PIV measurement algorithm to calculate the velocity field.In this study,a novel real-time adaptive particle image velocity(RTA-PIV) method is proposed to accurately measure the instantaneous velocity field of an unsteady flow field.In the proposed closed-loop RTA-PIV method,a new correlation-filter-based PIV measurement algorithm is introduced to calculate the velocity field in real time.Then,a Kalman predictor model is established to predict the velocity of the next time instant and a suitable interval time can be determined.To adaptively adjust the interval time for capturing two particle images,a new high-speed frame-straddling vision system is developed for the proposed RTA-PIV method.To fully analyze the performance of the RTA-PIV method,we conducted a series of numerical experiments on ground-truth image pairs and on real-world image sequences.