Multi-Target Tracking and Occlusion Handling with Learned Variational Bayesian Clusters and a Social Force Model
It addresses the problem of robust multi-target tracking in video for applications like surveillance, but appears incremental as it builds on existing techniques with specific enhancements.
The paper tackles multiple human target tracking in video sequences with varying numbers of targets and occlusions, proposing an algorithm that combines variational Bayesian clustering and a social force model within a particle filter, and shows improved tracking performance compared to state-of-the-art methods on public datasets.
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.