Multi-cue based multi-target tracking with boosted MHT

Abstract

Tracking multiple objects is critical to automatic video content analysis and virtual reality. The major problem is how to solve data association problem when ambiguous observations are caused by objects in close proximity or occlusion. To tackle this problem, we propose a boosted multiple hypotheses tracking (BMHT) algorithm for multiobject tracking. Here, on-line boosting learning is adopted to enhance the discriminative property and enlarge search space of the generative tracker MHT. To make the tracker be more reliable, a multi-cue integration strategy is adopted to consider different kinds of features under the on-line boosting framework. In this paper, we integrate both appearance and motion pattern information. For simplicity, Haar-like features and optical flow are adopted. We test our BMHT tracker on several challenging video sequences that involve heavy occlusion and pose variations. Experimental results show that the proposed BMHT achieves good performance. © Springer International Publishing Switzerland 2013.