Study of Multi-target Tracking Algorithm Based on Mean-shift and Particle Filter

LISS 2014 ◽  
2015 ◽  
pp. 1717-1724
Author(s):  
Lijing Huang ◽  
Naiwen Yu ◽  
Ming Han ◽  
Peng Liu
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Multi Target Tracking

Research on Target Tracking Algorithm Based on Particle Filter and Mean-Shift

2013 ◽  
Vol 457-458 ◽  
pp. 1050-1053
Author(s):  
Yan Hai Wu ◽  
Xia Min Xie ◽  
Zi Shuo Han
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Mean Shift ◽  
Extreme Value ◽  
Tracking Algorithm ◽  
Real Time Tracking ◽  
The Mean ◽  
Particle Filter Algorithm ◽  
Local Extreme

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.

Efficient Multi-Target Tracking Based on Meanshift and PNN

2013 ◽  
Vol 380-384 ◽  
pp. 3946-3949
Author(s):  
Zhi Ming Wang
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Video Analysis ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Background Model ◽  
Probability Neural Network ◽  
Multi Target Tracking

Multi-target tracking is one of the basic and difficult tasks in video analysis and understanding. This paper proposed an efficient tracking algorithm based on meanshift algorithm and PNN (Probability Neural Network) background model. Firstly, PNN detection results were used to initialize targets for meanshift tracking. Secondly, in the succeeding frames, every target was matched to detected regions before tracking. At last, only targets which couldnt match with new regions need tracking with meanshift tracking algorithm. Experimental results show that mean search steps for every target were dramatically reduced compare with original mean shift tracking algorithm.

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