Fusion Tracking Algorithm for High Complexity Continuous Moving Image Based on Gray Pixel Space Reconstruction

Aiming at the problems of inaccurate interaction point position, interaction point drift, and interaction feedback delay in the process of LiDAR sensor signal processing interactive system, a target tracking algorithm is proposed by combining LiDAR depth image information with color images. The algorithm first fuses the gesture detection results of the LiDAR and the visual image and uses the color information fusion algorithm of the Camshift algorithm to realize the tracking of the moving target. The experimental results show that the multi-information fusion tracking algorithm based on this paper has achieved higher recognition rate and better stability and robustness than the traditional fusion tracking algorithm.

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RGB-infrared fusion tracking algorithm based on Siamese network

10.1117/12.2604555 ◽

2021 ◽

Author(s):

Ruyou Li ◽

Wennan Cui ◽

Zhiyong Wang ◽

Jingjing Zhang

Keyword(s):

Tracking Algorithm ◽

Siamese Network ◽

Fusion Tracking

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Fusion Tracking Algorithm Based on Stochastic Approximation

2006 IEEE International Conference on Information Acquisition ◽

10.1109/icia.2006.305833 ◽

2006 ◽

Cited By ~ 1

Author(s):

Liwei Guo ◽

Xueguang Chen ◽

Shiqiang Hu

Keyword(s):

Stochastic Approximation ◽

Tracking Algorithm ◽

Fusion Tracking

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An On-Line Adaptive Product Fusion Tracking Algorithm Based on Multiple-Cue

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.303-306.1536 ◽

2013 ◽

Vol 303-306 ◽

pp. 1536-1542

Author(s):

Jin Hua Wang ◽

De Qiang Fu ◽

Jie Cao

Keyword(s):

Object Tracking ◽

Tracking Algorithm ◽

Scene Change ◽

The Poor ◽

Adaptive Fusion ◽

On Line ◽

Fusion Tracking ◽

Simulation Results

To solve the poor robustness problem that object tracking only by fixation fusion based on multiple clues, an online adaptive fusion tracking algorithm based on multiple clues is proposed. the weighting of the different clues is online adjusting based on scene change , the more the feature contributes, the more a larger value, the less the feature contributes ,the less a smaller value. Simulation results show that this algorithm provides better robustness and stability.

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