Node Selection of Collaborative Tracking Based on Particle Filter

Abstract

Abstract: Due to the problem of target tracking in video sensor networks, a new algorithm was proposed for cooperative monitoring and tracking of node selection based on particle filter. The method obtained posterior distribution through particle filtering in tracking problem and got the information entropy to evaluate the estimate uncertainty. Background modeling and the phase division were applied to extract target blob, and pixels number of the target blob was calculated to measure detection information. Confidence measure of nodes was determined based on the two factors to realize the optimization of the selected node and then track by particle filter. Experiment results show that the proposed method can effectively improve the tracking accuracy in comparison with the similar method, which selects camera nodes to achieve.

Key words: node selection, cooperative tracking, blob extraction, particle filter, confidence measure, information entropy

CLC Number:

TP391

Chen Ying, Cheng Xuexue. Node Selection of Collaborative Tracking Based on Particle Filter[J]. Journal of System Simulation, 2016, 28(6): 1281-1288.

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