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Multiple object tracking via species-based particle swarm optimization

Zhang, X. and Hu, W. and Qu, W. and Maybank, Stephen J. (2010) Multiple object tracking via species-based particle swarm optimization. IEEE Transactions on Circuits and Systems for Video Technology 20 (11), pp. 1590-1602. ISSN 1051-8215.

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Multiple object tracking is particularly challenging when many objects with similar appearances occlude one another. Most existing approaches concatenate the states of different objects, view the multi-object tracking as a joint motion estimation problem and search for the best state of the joint motion in a rather high dimensional space. However, this centralized framework suffers from a high computational load. We bring a new view to the tracking problem from a swarm intelligence perspective. In analogy with the foraging behavior of bird flocks, we propose a species-based particle swarm optimization algorithm for multiple object tracking, in which the global swarm is divided into many species according to the number of objects, and each species searches for its object and maintains track of it. The interaction between different objects is modeled as species competition and repulsion, and the occlusion relationship is implicitly deduced from the "power" of each species, which is a function of the image observations. Therefore, our approach decentralizes the joint tracker to a set of individual trackers, each of which tries to maximize its visual evidence. Experimental results demonstrate the efficiency and effectiveness of our method.

Item Type: Article
Keyword(s) / Subject(s): Multiple object tracking, particle swarm optimization
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Administrator
Date Deposited: 24 May 2011 12:48
Last Modified: 11 Sep 2013 14:48

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