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

DOI10.1109/TSP.2015.2504340zbMATH Open1412.94127arXiv1511.01726MaRDI QIDQ4618235

Ata ur Rehman, Lyudmila Mihaylova, Syed Mohsen Naqvi, Jonathon A. Chambers

Publication date: 7 February 2019

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

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.

Full work available at URL: https://arxiv.org/abs/1511.01726

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Detection theory in information and communication theory (94A13)