Human tracking using particle filter with Reliable Appearance Model

In this paper, we present a human tracking algorithm that can work robustly in complex environments such that serious occlusion, various appearances and abrupt motion changes occur in the scenario. Our tracking framework is well known particle filter based on Condensation algorithm. In the observation model of the particle filter, we establish RAM(Reliable Appearance Model) which exhibits high discriminative performance in particular for human tracking. The RAM is to describe a target as features from local descriptors. In order to extract practical features from a larger number of local descriptors for robust tracking, the features were employed by boosting algorithm. The components of the features are utilized color and shape based-models. Experimental results demonstrate that our approach tracks the target accurately and reliably when position and scale are changing as well as occurrence of occlusion.