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

arXiv: https://arxiv.org/abs/1511.01726 · HTML: https://mlsanity.com/paper/1511.01726/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2015.

## Built on by (most cited first, 1 of 1)

- [Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets](https://mlsanity.com/paper/2407.08872/) (2024)

## Abstract (arXiv)

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.