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CVPR
2012
IEEE

Decentralized particle filter for joint individual-group tracking

8 years 2 months ago
Decentralized particle filter for joint individual-group tracking
In this paper, we address the task of tracking groups of people in surveillance scenarios. This is a major challenge in computer vision, since groups are structured entities, subjected to repeated split and merge events. Our solution is a joint individual-group tracking framework, inspired by a recent technique dubbed decentralized particle filtering. The proposed strategy factorizes the joint individual-group state space in two dependent subspaces where individuals and groups share the knowledge of the joint individualgroup distribution. In practice, we establish a tight relation of mutual support between the modeling of individuals and that of groups, promoting the idea that groups are better tracked if individuals are considered, and viceversa. Extensive experiments on a published and novel dataset validate our intuition, opening up to many future developments.
Loris Bazzani, Marco Cristani, Vittorio Murino
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Loris Bazzani, Marco Cristani, Vittorio Murino
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