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Erschienen in:
2016 | OriginalPaper | Buchkapitel
A New Weight Adjusted Particle Swarm Optimization for Real-Time Multiple Object Tracking
verfasst von : Guang Liu, Zhenghao Chen, Henry Wing Fung Yeung, Yuk Ying Chung, Wei-Chang Yeh
Erschienen in: Neural Information Processing
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Abstract
This paper proposes a novel Weight Adjusted Particle Swarm Optimization (WAPSO) to overcome the occlusion problem and computational cost in multiple object tracking. To this end, a new update strategy of inertia weight of the particles in WAPSO is designed to maintain particle diversity and prevent pre-mature convergence. Meanwhile, the implementation of a mechanism that enlarges the search space upon the detection of occlusion enhances WAPSO’s robustness to non-linear target motion. In addition, the choice of Root Sum Squared Errors as the fitness function further increases the speed of the proposed approach. The experimental results has shown that in combination with the model feature that enables initialization of multiple independent swarms, the high-speed WAPSO algorithm can be applied to multiple non-linear object tracking for real-time applications.