Research on target tracking algorithm based on mean shift with adaptive bandwidth

Author(s):  
Ming Han ◽  
Jingqin Wang ◽  
Jingtao Wang ◽  
Junying Meng ◽  
Ying Cheng

The traditional mean shift algorithm used fixed kernels or symmetric kernel function, which will cause the target tracking lost or failure. The target tracking algorithm based on mean shift with adaptive bandwidth was proposed. Firstly, the signed distance constraint function was introduced to produce the anisotropic kernel function based on signed distance kernel function. This anisotropic kernel function satisfies that the value of the region function outside the target is zero, which provides accurate tracking window for the target tracking. Secondly, calculate the mean shift window center of anisotropic kernel function template, the theory basis is the sum of vector weights from the sample point in the tracking window to the center point is zero. Thirdly, anisotropic kernel function templates adaptive update implementation by similarity threshold to limit the change of the template between two sequential pictures, so as to realize real-time precise tracking. Finally, the contrast experimental results show that our algorithm has good accuracy and high real time.

2009 ◽  
Vol 29 (6) ◽  
pp. 1680-1682
Author(s):  
Chang-tao CHEN ◽  
Qin ZHU ◽  
Sheng-yi ZHOU ◽  
Jia-ming ZHANG

Author(s):  
Zhipeng Li ◽  
Xiaolan Li ◽  
Ming Shi ◽  
Wenli Song ◽  
Guowei Zhao ◽  
...  

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.


2014 ◽  
Vol 556-562 ◽  
pp. 4260-4263
Author(s):  
Bing Yun Dai ◽  
Hui Zhao ◽  
Zheng Xi Kang

Target tracking algorithm mean-shift and kalman filter does well in tracking target. However, mean-shift algorithm may not do well in tracking the target which the size of target is changing gradually. Although some scholars put forward by 10% of the positive and negative incremental to scale adaptive,the algorithm can not be applied to track the target which gradually becomes bigger. In this paper, we propose registration corners of the target of the two adjacent frames, then calculate the distance ratio of registration corners.Use the distance ratio to determine the target becomes larger or smaller. The experimental results demonstrate that the proposed method performs better compared with the recent algorithms.


2013 ◽  
Vol 411-414 ◽  
pp. 1322-1325
Author(s):  
Ya Hui Hu ◽  
Le Jiang Guo ◽  
Xiao Lei ◽  
Cheng Min

This paper selects the target tracking algorithm suitable for specific target environment: using Mean Shift algorithm based on space edge direction histogram at initialization, selecting tracking algorithm based on block when there is a shelter. On the basis of algorithm analysis and software experiment and studying of TI Company's TMS320DM642 DSP chip internal structure and development process, these two algorithms researched in this paper were transplanted to DSP platform and a series of optimization were been made to the algorithms codes after transplanted ,implementing target tracking and identified via DSP development board instead of PC.


2013 ◽  
Vol 457-458 ◽  
pp. 1050-1053
Author(s):  
Yan Hai Wu ◽  
Xia Min Xie ◽  
Zi Shuo Han

Since Mean-Shift tracking algorithm always falls into local extreme value when the target was sheltered and the particle filter tracking algorithm has huge calculation and degeneracy phenomenon, a new target tracking algorithm based on Mean-Shift and Particle Filter combination is proposed in this paper. First, this paper introduces the basic theory of Mean-Shift and Particle Filter tracking algorithm, and then presents the new target tracking which the Mean-Shift iteration embeds Particle Filter algorithm. Experiment results show that the algorithm needs less computation, while the real-time tracking has been guaranteed, robustness has been improved and the tracking results has been greatly increased.


2013 ◽  
Vol 475-476 ◽  
pp. 947-951
Author(s):  
Zhi Yuan Mai ◽  
Kun Yu Tan ◽  
An Ting Xu ◽  
Wei Xiang

The tracking effect is not good for the faster track with Mean Shift tracking algorithm when the difference is not obvious between the track target and background pixels in the video of global visual robotic fish.To solve the difficulty of tracking drastically moving targets in this paper, determining the position of moving targets in the next frame through comparing with two bc coefficients which have been set when the Epanechnikov has been selected core to estimate is indeed. The experimental results show the proposed algorithm can track the moving targets efficiently and precisely in video,and also can meet high real-time situation with small calculation.


2017 ◽  
Vol 139 ◽  
pp. 00057
Author(s):  
ZhuoYa Wang ◽  
Yaohong Qu ◽  
Jiaju Wu ◽  
Bing Xiao ◽  
Jianguo Yan

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