NDLI: A hybrid Particle Swarm Optimization with cooperative method for multi-object tracking

Content Provider	IEEE Xplore Digital Library
Author	Zheng Zhang Hock Soon Seah Jixiang Sun
Copyright Year	2012
Description	Author affiliation: School of Computer Engineering, Nanyang Technological University, Singapore (Zheng Zhang; Hock Soon Seah) \|\| College of Electronic Science and Engineering, National University of Defense Technology, China (Jixiang Sun)
Abstract	In the fields of computer vision, multiple object tracking is an active research area. It is a challenging problem mainly due to the frequent occlusions and interactions that happen between the multiple targets. We formulate the multiple interaction problem as an optimization problem and explore Particle Swarm Optimization (PSO) algorithm for the optimal solution. To tackle the problem of premature convergence, we present a new hybrid PSO that incorporates a differential evolution mutation operation with a Gaussian based PSO. Furthermore, by exploiting the specific structure of multiple object interactions, we introduce a cooperative strategy into the proposed PSO for more efficient searching and for conquering the curse of dimensionality. With patch-based observation models, our method can robustly handle significant occlusions and interactions.
Starting Page	1
Ending Page	6
File Size	1768973
Page Count	6
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6256414
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Joints Computational modeling Optimization Search problems Vectors Equations
Content Type	Text
Resource Type	Article

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