Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96729
Title: A hybrid particle swarm optimization with cooperative method for multi-object tracking
Authors: Zhang, Zheng
Seah, Hock Soon
Sun, Jixiang
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Zhang, Z., Seah, H. S., & Sun, J. (2012). A hybrid Particle Swarm Optimization with cooperative method for multi-object tracking. 2012 IEEE Congress on Evolutionary Computation (CEC).
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.
URI: https://hdl.handle.net/10356/96729
http://hdl.handle.net/10220/12005
DOI: 10.1109/CEC.2012.6256414
Rights: © 2012 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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