Multi-cue based multi-target tracking using online random forests
Discriminative tracking has become popular tracking methods due to their descriptive power for foreground/background separation. Among these methods, online random forest is recently proposed and received a large amount of research attention due to its advantages such as efficiency and robustness to noise, etc. However, the fact that only one kind of features is used limits the discriminative performance of this tracker. Additionally, the standard online forest tracker works only for a single target object. In this paper, we introduce a novel tracking method that integrates multiple cues capturing both geometric structures and edge-based shape information. Compared with the current online random forest based tracking algorithm, the proposed multi-cue tracker is more robust thanks to the complimentary information provided from these hybrid cues. Furthermore, the new tracker can track multiple targets as well as single target object. The effectiveness of the proposed tracker is validated using five public sequences.