Fusion Tracking Algorithm of Mean-shift and Particle Filter Based on EMD

2012 ◽  
Author(s):  
Xiaohao Li ◽  
Fuchun Sun ◽  
Yuan Yuan Liu
Keyword(s):  
Particle Filter ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Fusion 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.

A Multi-Feature Fusion Tracking Method Based on Mean Shift and Particle Filter

2014 ◽  
Vol 701-702 ◽  
pp. 257-260
Author(s):  
Ming Jie Zhang ◽  
Bao Sheng Kang
Keyword(s):  
Particle Filter ◽  
Feature Fusion ◽  
Mean Shift ◽  
Filter Method ◽  
Complex Environments ◽  
Shape Information ◽  
Shift Method ◽  
Tracking Method ◽  
Fusion Tracking ◽  
Stability And Accuracy

In order to improve the robustness of visual tracking in complex environments, a novel multi-feature fusion tracking method based on mean shift and particle filter is proposed. In the proposed method, the color and shape information are adaptively fused to represent the target observation, and incorporating mean shift method into particle filter method. The method can overcome the degeneracy problem of particle. Experimental results demonstrate that this method can improve stability and accuracy of tracking.

Robust tracking algorithm using mean-shift and particle filter

2011 ◽  
Author(s):  
Jianhua Wang ◽  
Wei Liang
Keyword(s):  
Particle Filter ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Robust Tracking

A Visual Tracking Based on Particle Filter of Multi-Algorithm Fusion

2014 ◽  
Vol 513-517 ◽  
pp. 2893-2896
Author(s):  
Tao Li ◽  
Qi Yuan Sun
Keyword(s):  
Particle Filter ◽  
Visual Tracking ◽  
Mean Shift ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Mutation Operator ◽  
Genetic Evolution ◽  
Tracking Accuracy ◽  
Crossover And Mutation

A novel visual tracking algorithm based on particle filter with multi-algorithm fusion is proposed. Mean shift is employed to make particles distribute more reasonably in order to maintain tracking accuracy by using fewer particles, and the genetic evolution ideas is introduced to increase the diversity of samples by applying selection, crossover and mutation operator to achieve particles resampling. The experiments show that the tracking performance of the proposed method, compared with Mean Shift Embedded Particle Filter (MSEPF), is significantly improved.

scholarly journals Moving Target Localization in Sports Image Sequence Based on Optimized Particle Filter Hybrid Tracking Algorithm

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yan Guo
Keyword(s):  
Particle Filter ◽  
Template Matching ◽  
High Weight ◽  
Mean Shift ◽  
Dead Reckoning ◽  
Image Sequence ◽  
Tracking Algorithm ◽  
Absolute Position ◽  
Optimal Position ◽  
Switching Algorithm

This paper proposes a fusion stadium positioning algorithm, which uses self-optimizing particle filter to integrate the improved athlete dead reckoning and WiFi position fingerprint algorithm for stadium positioning. In order to determine the initial absolute position of the stadium positioning, for athletes entering the stadium from the outside, a seamless switching algorithm outside the stadium is proposed, using the characteristics of high-altitude satellite GPS to find a suitable switching point as the initial absolute position. If in the stadium, WiFi static positioning determines the initial absolute position. Then, aiming at the problem that the poorly diversified particles cannot be better integrated and localized, a self-optimized particle filter algorithm is proposed. After resampling and retaining high-weight particles, the characteristics of low-weight particles are embedded in the copied high-weight particles. This can improve diversity, and we finally carry out fusion positioning. The target tracking algorithm based on Mean Shift has a fixed-scale tracking window, and the tracking effect of variable-size targets is not ideal. In this paper, an affine transformation algorithm is introduced to improve it. First, we iterate the adjacent image frames in reverse Mean Shift to determine the center position of the target and then use the corner matching method to perform template matching on the target to adjust the size of the tracking window. Through simulation verification, it is proved that the optimized particle filter hybrid tracking algorithm can achieve the ideal result when the target size changes. For the image sequence S1, the tracking window of the 20th frame and the 40th frame has a small offset, but the optimal position can be quickly found by Mean Shift iteration. For the image sequence S2, between the 40th frame and the 60th frame, the target occlusion causes the accuracy of the target template to decrease, and the Bhattacharyya coefficient is at a relatively low value. For the image sequence S3, the tracking effect of the optimized particle filter hybrid tracking algorithm meets the requirements.

A New Tracking Algorithm Based on Mean Shift and Particle Filter

2016 ◽  
Author(s):  
Liu Zongang ◽  
Du Xiaoxue ◽  
Pan Diansheng
Keyword(s):  
Particle Filter ◽  
Mean Shift ◽  
Tracking Algorithm

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
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