NDLI: Adaptive particle swarm optimization-based particle filter for tracking maneuvering object

Content Provider	IEEE Xplore Digital Library
Author	Qie Zhian Li Jianxun Zhang Yong
Copyright Year	2014
Description	Author affiliation: Sch. of Electron. Inf. & Electr. Eng., Shanghai Jiaotong Univ., Shanghai, China (Qie Zhian; Li Jianxun; Zhang Yong)
Abstract	Particle filter is a powerful tool for visual tracking based on Sequential Monte Carlo framework. However,it is faced with a fatal problem due to its suboptimal sampling mechanism in the importance sampling process and thus leads to the well-know sample impoverishment problem. Although last decade people have developed a lot resampling schemes to solve this problem, no one can be suitable for all situation. The standard PSO-PF algorithm, which applying particle swarm optimization algorithm into particle filter framework, is an excellent one of them. However, we found that the standard PSO-PF not suitable for tracking object with complicated motion model such as maneuvering object, which is moving with various motion state such as abrupt motion or mildly motion or even stand still. In this paper, we propose an algorithm, which we call it Adaptive Particle Swarm Optimization-Based Particle Filter(APSO-PF), to track the maneuvering object. Instead of constant parameters used in the standard PSO-PF, APSO-PF can adaptively change its parameters according to the motion state of the object and thus improve the tracking performance significantly. Experimental results show that our algorithm surpasses the standard PSO-PF on both robustness and accuracy.
Starting Page	4685
Ending Page	4690
File Size	680303
Page Count	6
File Format	PDF
ISBN	9789881563873
ISSN	19341768
DOI	10.1109/ChiCC.2014.6895729
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-28
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	TCCT, CAA
Subject Keyword	Algorithm design and analysis Visualization Tracking Particle Filter Heuristic algorithms Object Tracking Robustness Adaptive Particle Swarm Optimization Particle swarm optimization Standards
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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