Efficient Multi-Target Tracking Method Based on Fusion Morphology Filtering in Traffic Video Detection

2011 ◽  
Vol 179-180 ◽  
pp. 1412-1416
Author(s):  
Wei Bin Chen ◽  
Zhi Yang Gu ◽  
Xin Zhang
Keyword(s):  
Target Tracking ◽  
Mean Shift ◽  
Tracking Method ◽  
Mean Shift Algorithm ◽  
Video Frames ◽  
Analysis Technique ◽  
Multi Target Tracking ◽  
Morphological Operator ◽  
Real Video ◽  
Morphology Filter

Aiming at the tracking real-time and accuracy problem of traffic videos, a efficient Multi-target tracking method based on Fusion morphology filter is proposed. The proposed Multi-target tracking method used by Morphologically Fused Tracking approach and combined with pyramid mean shift algorithm, quantizes the deterioration effects encountered on marble surfaces by providing the number, location and shape of deterioration spots. First the detection based on the application of morphological operator sequential filters by reconstruction with structuring elements of growing size. Then adopt a traditional tracking method such as adaptive pyramid mean shift, because it uses the pyramid analysis technique and can determine the pyramid level adaptively to decrease the number of iterations required to achieve region. We validate our approach on tracking examples using real video sequences, it can precisely partition and track the predetermined objects in video frames.

Digital Image Stabilization Algorithm Based on Mean Shift Corner Tracking

2011 ◽  
Vol 130-134 ◽  
pp. 3155-3157
Author(s):  
Jian Peng ◽  
Qing Min ◽  
Zhi Qiang Xu
Keyword(s):  
Motion Estimation ◽  
Target Tracking ◽  
Digital Image ◽  
Motion Compensation ◽  
Mean Shift ◽  
Image Stabilization ◽  
Tracking Method ◽  
Mean Shift Algorithm ◽  
The Core ◽  
Core Technology

The core technology of electronic ois is the motion estimation techniques, such as, BPM, RPM, FTA ,PA, BM and so on[1]. Based on the excellent tracking performance of Mean Shift algorithm in complicated scene, this paper proposed a target tracking method based on corner, and then work on motion compensation and motion estimation.

Video Vehicle Tracking Based on Improved Mean-Shift Algorithm

2011 ◽  
Vol 179-180 ◽  
pp. 1408-1411
Author(s):  
Wei Bin Chen ◽  
Xin Zhang ◽  
Su Qin Luo
Keyword(s):  
Clustering Algorithm ◽  
Mean Shift ◽  
Vehicle Tracking ◽  
Absolute Difference ◽  
Mean Shift Algorithm ◽  
Invariant Moments ◽  
Mean Shift Clustering ◽  
Video Frames ◽  
Detection Technology ◽  
Background Area

An improved Mean-Shift-based Video vehicle tracking algorithm was proposed and which can improve the real-time and accuracy of the vehicle detection technology in the application. First, it eliminates the disturbance from unrelated background by mathematical morphology operation between a traffic image and the mask of fixed background area .Then the image sequences are simulated by absolute difference of adaptive threshold for detecting latent target. At last, clusters video frames with similar characteristics which are regarded of the invariant moments vectors by Mean Shift clustering algorithm. Experimental results shown that the improved algorithm has advantages of reducing king region of vehicle matching and vehicle complete occlusion.

Tracking algorithm of snowboard target in intelligent system

2020 ◽  
pp. 1-9
Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  
Keyword(s):  
Target Tracking ◽  
High Speed ◽  
Intelligent System ◽  
Mean Shift ◽  
Target Position ◽  
Tracking Algorithm ◽  
Tracking Accuracy ◽  
Mean Shift Algorithm ◽  
Target Track ◽  
The Mean

Snowboarding is a kind of sport that takes snowboarding as a tool, swivels and glides rapidly on the specified slope line, and completes all kinds of difficult actions in the air. Because the sport is in the state of high-speed movement, it is difficult to direct guidance during the sport, which is not conducive to athletes to find problems and correct them, so it is necessary to track the target track of snowboarding. The target tracking algorithm is the main solution to this task, but there are many problems in the existing target tracking algorithm that have not been solved, especially the target tracking accuracy in complex scenes is insufficient. Therefore, based on the advantages of the mean shift algorithm and Kalman algorithm, this paper proposes a better tracking algorithm for snowboard moving targets. In the method designed in this paper, in order to solve the problem, a multi-algorithm fusion target tracking algorithm is proposed. Firstly, the SIFT feature algorithm is used for rough matching to determine the fuzzy position of the target. Then, the good performance of the mean shift algorithm is used to further match the target position and determine the exact position of the target. Finally, the Kalman filtering algorithm is used to further improve the target tracking algorithm to solve the template trajectory prediction under occlusion and achieve the target trajectory tracking algorithm design of snowboarding.

Research on Face Tracking in Community Monitoring System

2014 ◽  
Vol 484-485 ◽  
pp. 358-362
Author(s):  
Shuang Liu
Keyword(s):  
Target Tracking ◽  
Skin Color ◽  
Mean Shift ◽  
Texture Features ◽  
Mean Shift Algorithm ◽  
Local Character ◽  
Community Monitoring ◽  
The Mean ◽  
Target Block ◽  
Time Update

This paper proposes a block Mean-Shift algorithm based on target real-time update and LBP texture features, through the target update improves the accuracy of target tracking, enhances the local character of the target through the target block, so as to improve the robustness of algorithm based on skin color backgrounds. And then analyze the Mean-Shift algorithm cannot recover quickly lost target tracking defects, and its improvement by combining the frame difference method.

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