A Human Target Detection and Tracking Method Based on Adaptive Difference and GVF-Snake Algorithm

2013 ◽  
Vol 455 ◽  
pp. 344-349
Author(s):  
Xue Zhang ◽  
Wei Cheng Xie ◽  
Chao Huang ◽  
Qiang Xu
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Initial Contour ◽  
Differential Detection ◽  
Body Contour ◽  
Snake Model ◽  
Tracking Method ◽  
Detection And Tracking ◽  
Frame Difference ◽  
Snake Algorithm

Detection of Frame difference fails when the human target is stationary in course of moving, this paper presents a method based on combination of adaptive difference and GVF-snake algorithm to solve it. Adaptive differential detection algorithm can accurately extract the target contour, and use it as the initial contour of GVF-snake model which cannot automatically extract it after we got the target. In the process of detection and tracking, calculating GVF field of the whole picture consume too much time, so we use the method of sub-region to improve the real-time. The experimental results show that, the algorithm can provide the actual body contour for GVF-snake model, and effectively track whether the target is stationary or moving.

scholarly journals Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Renzheng Xue ◽  
Ming Liu ◽  
Xiaokun Yu
Keyword(s):  
Target Detection ◽  
Difference Method ◽  
Moving Objects ◽  
Target Location ◽  
Mean Shift ◽  
Detection Algorithm ◽  
Moving Target ◽  
Size Change ◽  
Detection And Tracking ◽  
Frame Difference

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.

Detection and Tracking Cows by Computer Vision and Image Classification Methods

2021 ◽  
Vol 13 (1) ◽  
pp. 1-45
Author(s):  
Terry Gao
Keyword(s):  
Feature Fusion ◽  
Binary Classification ◽  
Classification Problem ◽  
Detection Algorithm ◽  
Video Sequences ◽  
Image Block ◽  
Body Contour ◽  
Adaboost Algorithm ◽  
Target Model ◽  
Detection And Tracking

In this paper, the cow recognition and traction in video sequences is studied. In the recognition phase, this paper does some discussion and analysis which aim at different classification algorithms and feature extraction algorithms, and cow's detection is transformed into a binary classification problem. The detection method extracts cow's features using a method of multiple feature fusion. These features include edge characters which reflects the cow body contour, grey value, and spatial position relationship. In addition, the algorithm detects the cow body through the classifier which is trained by Gentle Adaboost algorithm. Experiments show that the method has good detection performance when the target has deformation or the contrast between target and background is low. Compared with the general target detection algorithm, this method reduces the miss rate and the detection precision is improved. Detection rate can reach 97.3%. In traction phase, the popular compressive tracking (CT) algorithm is proposed. The learning rate is changed through adaptively calculating the pap distance of image block. Moreover, the update for target model is stopped to avoid introducing error and noise when the classification response values are negative. The experiment results show that the improved tracking algorithm can effectively solve the target model update by mistaken when there are large covers or the attitude is changed frequently. For the detection and tracking of cow body, a detection and tracking framework for the image of cow is built and the detector is combined with the tracking framework. The algorithm test for some video sequences under the complex environment indicates the detection algorithm based on improved compressed perception shows good tracking effect in the changing and complicated background.

scholarly journals High-speed moving target detection and tracking method based on speed estimation

2019 ◽  
Vol 2019 (20) ◽  
pp. 6637-6641
Author(s):  
Jinquan Zhang ◽  
Jingwen Li ◽  
Haizhong Ma ◽  
Ye Wang
Keyword(s):  
Target Detection ◽  
High Speed ◽  
Moving Target ◽  
Speed Estimation ◽  
Moving Target Detection ◽  
Tracking Method ◽  
Detection And Tracking

scholarly journals A New Maritime Moving Target Detection and Tracking Method for Airborne Forward-looking Scanning Radar

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1586
Author(s):  
Weibo Huo ◽  
Jifang Pei ◽  
Yulin Huang ◽  
Qian Zhang ◽  
Jianyu Yang
Keyword(s):  
Likelihood Ratio ◽  
Target Detection ◽  
Moving Target ◽  
Sea Clutter ◽  
Moving Target Detection ◽  
Tracking Method ◽  
Ratio Calculation ◽  
Detection And Tracking ◽  
Spectral Residual ◽  
Forward Looking

Maritime moving target detection and tracking through particle filter based track-before-detect (PF-TBD) has significant practical value for airborne forward-looking scanning radar. However, villainous weather and surging of ocean waves make it extremely difficult to accurately obtain a statistical model for sea clutter. As the likelihood ratio calculation in PF-TBD is dependent on the distribution of the clutter, the performance of traditional distribution-based PF-TBD seriously declines. To resolve these difficulties, this paper proposes a new target detection and tracking method, named spectral-residual-binary-entropy-based PF-TBD (SRBE-PF-TBD), which is independent from the prior knowledge of sea clutter. In the proposed method, the likelihood ratio calculation is implemented by first extracting the spectral residual of the input image to obtain the saliency map, and then constructing likelihood ratio through a binarization processing and information entropy calculation. Simulation results show that the proposed method had superior performance of maritime moving target detection and tracking.

Research on athlete training behavior based on improved support vector algorithm and target image detection

2020 ◽  
Vol 39 (4) ◽  
pp. 5725-5736
Author(s):  
Jiang Min
Keyword(s):  
Target Detection ◽  
Training Model ◽  
Detection Algorithm ◽  
Support Vector ◽  
Svm Classifier ◽  
Target Image ◽  
Classification Result ◽  
Detection And Tracking ◽  
Neural Network Algorithm ◽  
Fully Connected

In view of the defects and shortcomings of the traditional target detection and tracking algorithm in accurately detecting targets and targets in different scenarios, based on the current research status and technical level of target detection and tracking at home and abroad, this paper proposes a target detection algorithm and tracking method using neural network algorithm, and applies it to the athlete training model. Based on the Alex-Net network structure, this paper designs a three-layer convolutional layer and two layers of fully connected layers. The last layer is used as the input of the SVM classifier, and the target classification result is obtained by the SVM classifier. In addition, this article adds SPP-Layer between the convolutional layer and the fully connected layer, enabling the same dimension of the Feature Map to be obtained before the fully connected layer for different sized input images. The research results show that the proposed method has certain recognition effect and can be applied to athlete training.

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