Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model
Abstract
This paper considers the problem of multiple human target tracking in a sequence of video data. A solution is proposed which is able to deal with the challenges of a varying number of targets, interactions and when every target gives rise to multiple measurements. The developed novel algorithm comprises variational Bayesian clustering combined with a social force model, integrated within a particle filter with an enhanced prediction step. It performs measurement-to-target association by automatically detecting the measurement relevance. The performance of the developed algorithm is evaluated over several sequences from publicly available data sets: AV16.3, CAVIAR and PETS2006, which demonstrates that the proposed algorithm successfully initializes and tracks a variable number of targets in the presence of complex occlusions. A comparison with state-of-the-art techniques due to Khan et al., Laet et al. and Czyz et al. shows improved tracking performance.
Cite
@article{arxiv.1511.01726,
title = {Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model},
author = {Ata-ur-Rehman and Syed Mohsen Naqvi and Lyudmila Mihaylova and Jonathon Chambers},
journal= {arXiv preprint arXiv:1511.01726},
year = {2015}
}
Comments
19 pages, 14 figures