Human detection using HOG-HSC feature and PLS

By combining histogram of oriented gradient and histograms of shearlet coefficients, which analyzes images at multiple scales and orientations based on shearlet transforms, as the feature set, we proposed a novel haman detection feature. We employ partial least squares analysis, an efficient dimensionality reduction technique, to project the feature onto a much lower dimensional subspace. We test it in INRIA person dataset by using a linear SVM, and it yields an error rate of 1.38% with a false negatives （FN） rate of 0.40% and a false positive （FP） rate of 0.98%, while the error rate of HOG is 7.11%, with a FN rate of 4.09% and a FP rate of 3.02%.

An efficient human detection method for multi-pedestrian tracking

Traditional human detection using pre-trained detectors tends to be computationally intensive for time-critical tracking tasks, and the detection rate is prone to be unsatisfying when occlusion, motion blur and body deformation occur frequently. A spatial-confidential proposal filtering method(SCPF) is proposed for efficient and accurate human detection. It consists of two filtering phases: spatial proposal filtering and confidential proposal filtering. A compact spatial proposal is generated in the first phase to minimize the search space to reduce the computation cost. The human detector only estimates the confidence scores of the candidate search regions accepted by the spatial proposal instead of global scanning. At the second phase, each candidate search region is assigned with a supplementary confidence score according to their reliability estimated by the confidential proposal to reduce missing detections. The performance of the SCPF method is verified by extensive tests on several video sequences from available public datasets. Both quantitatively and qualitatively experimental results indicate that the proposed method can highly improve the efficiency and the accuracy of human detection.

Vision and Geolocation Data Combination for Precise Human Detection and Tracking in Search and Rescue Operations

In this paper, a study and evaluation of the combination of GPS/GNSS techniques and advanced image processing algorithms for distressed human detection, positioning and tracking, from a fully autonomous Unmanned Aerial Vehicle (UAV)-based rescue support system, are presented. In particular, the issue of human detection both on terrestrial and marine environment under several illumination and background conditions, as the human silhouette in water differs significantly from a terrestrial one, is addressed. A robust approach, including an adaptive distressed human detection algorithm running every N input image frames combined with a much faster tracking algorithm, is proposed. Real time or near-real-time distressed human detection rates achieved, using a single, low cost day/night NIR camera mounted onboard a fully autonomous UAV for Search and Rescue (SAR) operations. Moreover, the generation of our own dataset, for the image processing algorithms training is also presented. Details about both hardware and software configuration as well as the assessment of the proposed approach performance are fully discussed. Last, a comparison of the proposed approach to other human detection methods used in the literature is presented.

Thermal Infrared Salient Human Detection Model Combined with Thermal Features in Airport Terminal

Target detection in low light background is one of the main tasks of night patrol robots for airport terminal.However,if some algorithms can run on a robot platform with limited computing resources,it is difficult for these algorithms to ensure the detection accuracy of human body in the airport terminal. A novel thermal infrared salient human detection model combined with thermal features called TFSHD is proposed. The TFSHD model is still based on U-Net,but the decoder module structure and model lightweight have been redesigned. In order to improve the detection accuracy of the algorithm in complex scenes,a fusion module composed of thermal branch and saliency branch is added to the decoder of the TFSHD model. Furthermore,a predictive loss function that is more sensitive to high temperature regions of the image is designed. Additionally,for the sake of reducing the computing resource requirements of the algorithm,a model lightweight scheme that includes simplifying the encoder network structure and controlling the number of decoder channels is adopted. The experimental results on four data sets show that the proposed method can not only ensure high detection accuracy and robustness of the algorithm,but also meet the needs of real-time detection of patrol robots with detection speed above 40 f/s.

PLS-CCA Heterogeneous Features Fusion-based Low-resolution Human Detection Method for Outdoor Video Surveillance

In this paper, we focus on low-resolution human detection and propose a partial least squares-canonical correlation analysis （PLS-CCA） for outdoor video surveillance. The analysis relies on heterogeneous features fusion-based human detection method. The proposed method can not only explore the relation between two individual heterogeneous features as much as possible, but also can robustly describe the visual appearance of humans with complementary information. Compared with some other methods, the experimental results show that the proposed method is effective and has a high accuracy, precision, recall rate and area under curve （AUC） value at the same time, and offers a discriminative and stable recognition performance.

Pose estimation based on human detection and segmentation

We address the problem of 3D human pose estimation in a single real scene image. Normally, 3D pose estimation from real image needs background subtraction to extract the appropriate features. We do not make such assumption, In this paper, a two-step approach is proposed, first, instead of applying background subtraction to get the segmentation of human, we combine the segmentation with human detection using an ISM-based detector. Then, silhouette feature can be extracted and 3D pose estimation is solved as a regression problem. RVMs and ridge regression method are applied to solve this problem. The results show the robustness and accuracy of our method.

Performance study of feature descriptors for human detection on depth map

Depth map contains the space information of objects and is almost free from the influence of light,and it attracts many research interests in the field of machine vision used for human detection.Therefore,hunting a suitable image feature for human detection on depth map is rather attractive.In this paper,we evaluate the performance of the typical features on depth map.A depth map dataset containing various indoor scenes with human is constructed by using Microsoft’s Kinect camera as a quantitative benchmark for the study of methods of human detection on depth map.The depth map is smoothed with pixel filtering and context filtering so as to reduce particulate noise.Then,the performance of five image features and a new feature is studied and compared for human detection on the dataset through theoretic analysis and simulation experiments.Results show that the new feature outperforms other descriptors.

Human Verification over Activity Analysis via Deep Data Mining

Human verification and activity analysis(HVAA)are primarily employed to observe,track,and monitor human motion patterns using redgreen-blue(RGB)images and videos.Interpreting human interaction using RGB images is one of the most complex machine learning tasks in recent times.Numerous models rely on various parameters,such as the detection rate,position,and direction of human body components in RGB images.This paper presents robust human activity analysis for event recognition via the extraction of contextual intelligence-based features.To use human interaction image sequences as input data,we first perform a few denoising steps.Then,human-to-human analyses are employed to deliver more precise results.This phase follows feature engineering techniques,including diverse feature selection.Next,we used the graph mining method for feature optimization and AdaBoost for classification.We tested our proposed HVAA model on two benchmark datasets.The testing of the proposed HVAA system exhibited a mean accuracy of 92.15%for the Sport Videos in theWild(SVW)dataset.The second benchmark dataset,UT-interaction,had a mean accuracy of 92.83%.Therefore,these results demonstrated a better recognition rate and outperformed other novel techniques in body part tracking and event detection.The proposed HVAA system can be utilized in numerous real-world applications including,healthcare,surveillance,task monitoring,atomic actions,gesture and posture analysis.

Automatic Feature Point Detection and Tracking of Human Actions in Time-of-flight Videos

Detecting feature points on the human body in video frames is a key step for tracking human movements. There have been methods developed that leverage models of human pose and classification of pixels of the body image. Yet, occlusion and robustness are still open challenges. In this paper, we present an automatic, model-free feature point detection and action tracking method using a time-of-flight camera. Our method automatically detects feature points for movement abstraction. To overcome errors caused by miss-detection and occlusion, a refinement method is devised that uses the trajectory of the feature points to correct the erroneous detections. Experiments were conducted using videos acquired with a Microsoft Kinect camera and a publicly available video set and comparisons were conducted with the state-of-the-art methods. The results demonstrated that our proposed method delivered improved and reliable performance with an average accuracy in the range of 90 %.The trajectorybased refinement also demonstrated satisfactory effectiveness that recovers the detection with a success rate of 93.7 %. Our method processed a frame in an average time of 71.1 ms.

Combination of Loop-Mediated Isothermal Amplification Assay and Nested PCR for Detection of Borrelia burgdorferi sensu lato in Human Serum Samples

A set of universal loop-mediated isothermal amplification （LAMP） primers targeting the flo gene was designed to detect Borrelia burgdorferi sensu lato （B. burgdorferi s.I.） in human samples. The sensitivity of LAMP was 20 copies/reaction, and the assay did not detect false positives among 11 other related bacteria. A positive LAMP result was obtained for 9 of the 24 confirmed cases and for 12 of 94 suspected cases. The positive rate of LAMP was the same as that of nested PCR. The LAMP is a useful diagnostic method that can be developed for rapid detection of B. burgdorferi s.I. in human sera. Combination of the LAMP and nested PCR was more sensitive for detecting B. burgdorferi s.I. in human serum samples.

Abnormal event detection by a weakly supervised temporal attention network

Abnormal event detection aims to automatically identify unusual events that do not comply with expectation.Recently,many methods have been proposed to obtain the temporal locations of abnormal events under various determined thresholds.However,the specific categories of abnormal events are mostly neglect,which are important to help in monitoring agents to make decisions.In this study,a Temporal Attention Network(TANet)is proposed to capture both the specific categories and temporal locations of abnormal events in a weakly supervised manner.The TANet learns the anomaly score and specific category for each video segment with only video-level abnormal event labels.An event recognition module is exploited to predict the event scores for each video segment while a temporal attention module is proposed to learn a temporal attention value.Finally,to learn anomaly scores and specific categories,three constraints are considered:event category constraint,event separation constraint and temporal smoothness constraint.Experiments on the University of Central Florida Crime dataset demonstrate the effectiveness of the proposed method.

Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers.In this paper,we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance.Research have mostly focused the problem of human detection in thin crowd,overall behavior of the crowd and actions of individuals in video sequences.Vision based Human behavior modeling is a complex task as it involves human detection,tracking,classifying normal and abnormal behavior.The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e.,fill hole inside objects algorithm.Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm.The classification task is achieved using binary and multi class support vector machines.The proposed technique is validated through accuracy,precision,recall and F-measure metrics.

A method for detecting miners based on helmets detection in underground coal mine videos

In order to monitor dangerous areas in coal mines automatically,we propose to detect helmets from underground coal mine videos for detecting miners.This method can overcome the impact of similarity between the targets and their background.We constructed standard images of helmets,extracted four directional features,modeled the distribution of these features using a Gaussian function and separated local images of frames into helmet and non-helmet classes.Out experimental results show that this method can detect helmets effectively.The detection rate was 83.7%.

A closed-loop algorithm to detect human face using color and reinforcement learning

A closed-loop algorithm to detect human face using color information and reinforcement learning is presented in this paper. By using a skin-color selector, the regions with color "like" that of human skin are selected as candidates for human face. In the next stage, the candidates are matched with a face model and given an evaluation of the match degree by the matching module. And if the evaluation of the match result is too low, a reinforcement learning stage will start to search the best parameters of the skin-color selector. It has been tested using many photos of various ethnic groups under various lighting conditions, such as different light source, high light and shadow. And the experiment result proved that this algorithm is robust to the vary-ing lighting conditions and personal conditions.

Image sequence-based risk behavior detection of power operation inspection personnel

A novel image sequence-based risk behavior detection method to achieve high-precision risk behavior detection for power maintenance personnel is proposed in this paper.In this method,the original image sequence data is first separated from the foreground and background.Then,the free anchor frame detection method is used in the foreground image to detect the personnel and correct their direction.Finally,human posture nodes are extracted from each frame of the image sequence,which are then used to identify the abnormal behavior of the human.Simulation experiment results demonstrate that the proposed algorithm has significant advantages in terms of the accuracy of human posture node detection and risk behavior identification.

Fall detection system in enclosed environments based on single Gaussian model

In this paper,we propose an efficient fall detection system in enclosed environments based on single Gaussian model using the maximum likelihood method.Online video clips are used to extract the features from two cameras.After the model is constructed,a threshold is set,and the probability for an incoming sample under the single Gaussian model is compared with that threshold to make a decision.Experimental results show that if a proper threshold is set,a good recognition rate for fall activities can be achieved.

THE PRELIMINARY APPLICATION OF IN SITU HYBRIDI-ZATION IN DETECTING PROTO-ONCOGENES EXPRESSION IN HUMAN LEUKEMIC CELLS

An in situ hybridization technique with 35S labelled proto-oncogene probes (c-myc & c-fes) was used to detect their expression in bone marrow cells of 22 cases of leukemia of various types and immature granulocytes and erythroblasts of 16 nomal myelograms as controls. Both c-myc and c-fes were detectable in leukemic cells as well as in immature granulocytes and erythroblasts of normal bone marrow, but the expression extent varied in different cases. The levels of c-myc expression in leukemic cells were higher than those in controls (P<0.001). There was no difference of c-fes expression in two groups of bone marrow cells (P>0.05). This technique provides us a new method in studying variations of proto-oncogene expression in leukemic cells.

Elderly Fall Detection by Sensitive Features Based on Image Processing and Machine Learning

The world’s elderly population is growing every year.It is easy to say that the fall is one of the major dangers that threaten them.This paper offers a Trained Model for fall detection to help the older people live comfortably and alone at home.The purpose of this paper is to investigate appropriate methods for diagnosing falls by analyzing the motion and shape characteristics of the human body.Several machine learning technologies have been proposed for automatic fall detection.The proposed research reported in this paper detects a moving object by using a background subtraction algorithm with a single camera.The next step is to extract the features that are very important and generally describe the human shape and show the difference between the human falls from the daily activities.These features are based on motion,changes in human shape,and oval diameters around the human and temporal head position.The features extracted from the human mask are eventually fed in to various machine learning classifiers for fall detection.Experimental results showed the efficiency and reliability of the proposed method with a fall detection rate of 81%that have been tested with UR Fall Detection dataset.

Facile and Economical Fabrication of Superhydrophobic Flexible Resistive Strain Sensors for Human Motion Detection

Superhydrophobic flexible strain sensors have great application value in the fields of personal health monitoring,human motion detection,and soft robotics due to their good flexibility and high sensitivity.However,complicated preparation processes and costly processing procedures have limited their development.To overcome these limitations,in this work we develop a facile and low-cost method for fabricating superhydrophobic flexible strain sensor via spraying carbon black(CB)nanoparticles dispersed in a thermoplastic elastomer(SEBS)solution on a polydimethylsiloxane(PDMS)flexible substrate.The prepared strain sensor had a large water contact angle of 153±2.83°and a small rolling angle of 8.5±1.04°,and exhibited excellent self-cleaning property.Due to the excellent superhydrophobicity,aqueous acid,salt,and alkali could quickly roll off the flexible strain sensor.In addition,the sensor showed excellent sensitivity(gauge factor(GF)of 5.4–7.35),wide sensing ranges(stretching:over 70%),good linearity(three linear regions),low hysteresis(hysteresis error of 4.8%),and a stable response over 100 stretching-releasing cycles.Moreover,the sensor was also capable of effectively detecting human motion signals like finger bending and wrist bending,showing promising application prospects in wearable electronic devices,personalized health monitoring,etc.