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

TL;DR

This paper introduces a novel multi-target tracking algorithm that combines variational Bayesian clustering and a social force model within a particle filter, effectively handling occlusions, interactions, and varying target counts in video data.

Contribution

It presents a new algorithm integrating variational Bayesian clustering with a social force model in a particle filter for improved multi-target tracking.

Findings

Successfully tracks multiple targets with occlusions

Outperforms state-of-the-art methods on public datasets

Handles varying number of targets and complex interactions

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.