Kinematics Analysis of Aerobics Movement Decomposition Based on Multi-target Video Tracking Algorithm

2021 ◽  
pp. 21-28
Author(s):  
Peng Yang
Keyword(s):  
Video Tracking ◽  
Tracking Algorithm ◽  
Kinematics Analysis

Robust video tracking algorithm: a multi‐feature fusion approach

2018 ◽  
Vol 12 (5) ◽  
pp. 640-650 ◽  
Author(s):  
Howard Wang ◽  
Sing Kiong Nguang ◽  
Jiwei Wen
Keyword(s):  
Feature Fusion ◽  
Video Tracking ◽  
Tracking Algorithm ◽  
Fusion Approach

Texture Feature-Based Particle Filter Video Tracking Using Cooccurrence Matrices

2013 ◽  
Vol 457-458 ◽  
pp. 1294-1297
Author(s):  
Tao Zhang ◽  
Yu Qing Chen ◽  
Xiang Yu Yu
Keyword(s):  
Particle Filter ◽  
Texture Feature ◽  
Video Tracking ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Color Feature ◽  
Texture Information ◽  
Feature Based

.In this paper, a video tracking approach based on particle filter is proposed. Texture information is used instead of color. In the proposed approach, gray cooccurrence matrices are used as the texture metric. Experimental results show that the proposed algorithm lead to better result than color feature-based particle filter-based video tracking algorithm and is an effective tool for complicated video tracking application.

Markov chain Monte Carlo based video tracking algorithm

2017 ◽  
Vol 43 (4) ◽  
pp. 224-229 ◽  
Author(s):  
D. Kuplyakov ◽  
E. Shalnov ◽  
A. Konushin
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Video Tracking ◽  
Tracking Algorithm

Research of Video Tracking Algorithm Based on the Blob Analysis

2011 ◽  
Vol 383-390 ◽  
pp. 1185-1189
Author(s):  
Zeng Ping Zhang ◽  
Shu Hua Li
Keyword(s):  
Kalman Filter ◽  
Probability Distribution ◽  
Particle Filter ◽  
Posterior Probability ◽  
Video Tracking ◽  
Tracking Algorithm ◽  
Posterior Probability Distribution ◽  
Blob Analysis ◽  
Further Development ◽  
Target Locating

To the video that contains the target, a method is proposed to create the background model based on the mixed Gauss. And the target locating method based on the blob analysis and blob filtering, the anti-noise ability and filter robustness of tracking is improved. The kalman filter and the particle filter are separately used to pass and update the foreground target’s posterior probability distribution. Finally the kalman filter and the particle filter's are compared and that builds the foundation of the further development.

scholarly journals A New Video Tracking Algorithm Based on Multi-Complementary Features Fusion

2019 ◽  
Vol 37 (2) ◽  
pp. 323-329
Author(s):  
Junchang Zhang ◽  
Deng Zhang ◽  
Jinjin Wan
Keyword(s):  
Video Tracking ◽  
Tracking Algorithm ◽  
Experimental Simulation ◽  
Factor Graph ◽  
Object Model ◽  
Features Fusion ◽  
Time Performance ◽  
Tracking Process ◽  
Weight Coefficients ◽  
Predictive Response

In order to make full use of the diversity of sample information in the tracking process and improve the generalization ability of the tracker, this paper integrates the object model prediction results on the basis of the Staple algorithm, and applies weighted bands to the simple linearity of different predictive response results in the algorithm. To the uncertainties, a new adaptive response factor graph fusion method with weight coefficients is proposed, which effectively improves the reliability of the video target tracking algorithm. Theoretical analysis and experimental simulation show that the proposed algorithm is more accurate and robust than the classical Staple algorithm, and it maintains high real-time performance.

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