[1]CHEN Zhen,WANG Zhao.Object tracking algorithm with metacognitive model-based particle filters[J].CAAI Transactions on Intelligent Systems,2015,10(3):387-392.[doi:10.3969/j.issn.1673-4785.201405052]
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Object tracking algorithm with metacognitive model-based particle filters

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 10 Number of periods: 2015 3 Page number: 387-392 Column: 学术论文—机器学习 Public date: 2015-06-25
Title:
Object tracking algorithm with metacognitive model-based particle filters
Author(s):
CHEN Zhen;  WANG Zhao
College of Information and Control Engineering, China University of Petroleum, Qingdao 266580, China
Keywords:
object tracking; particle filter; metacognition; metacognitive model; metacognitive particle filter (MCPF)
CLC:
TP391
DOI:
10.3969/j.issn.1673-4785.201405052
Abstract:
Traditional particle filter object tracking in complex backgrounds cannot update the sudden backgrounds information to the target template timely. Therefore, it is easy to cause tracking instability, even lose object when the sudden background is similar to the target template. An object tracking algorithm with metacognitive particle filters (MCPF) is proposed to solve this problem. Firstly, MCPF establishes metacognitive knowledge composed of object cognitive models and background cognitive model. Next, MCPF monitors the observation information of the sampling particles after multiple iterations, simulates the observation information to generate metacognitive experience and realizes object-tracking stability. Extensive experimental results showed that the proposed algorithm improves the instantaneity, stability and reliability of traditional PF object tracking algorithm. When the background suddenly changes, it can timely monitor it and quickly adjust decision-making mechanism to realize stable target tracking.
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