Title :
Decentralized particle filter for joint individual-group tracking
Author :
Bazzani, Loris ; Cristani, Marco ; Murino, Vittorio
Author_Institution :
Univ. of Verona, Verona, Italy
Abstract :
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 individual-group 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.
Keywords :
computer vision; object tracking; particle filtering (numerical methods); surveillance; computer vision; decentralized particle filter; joint individual-group distribution; joint individual-group state space; joint individual-group tracking; split and merge event; surveillance scenario; Approximation methods; Joints; Monte Carlo methods; Probability distribution; Proposals; Standards; Target tracking;
Conference_Titel :
Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on
Conference_Location :
Providence, RI
Print_ISBN :
978-1-4673-1226-4
Electronic_ISBN :
1063-6919
DOI :
10.1109/CVPR.2012.6247888