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arXiv · 1511.01726

Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model

Ata-ur-Rehman·Syed Mohsen Naqvi·Lyudmila Mihaylova·Jonathon Chambers
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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.

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Ata-ur-Rehman, Syed Mohsen Naqvi, Lyudmila Mihaylova, Jonathon Chambers. 2015-11-05. Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model. https://arxiv.org/abs/1511.01726

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