Prototypes based discriminative appearance model for object tracking

Abstract

Occlusion is one of the major challenges for object tracking in real life scenario. Various techniques in particle filter framework have been developed to solve this problem. This framework depends on two issues: motion model and observation (likelihood) model. Due to the lack of effective observation model and efficient motion model, problem of occlusion still remains unsolvable in the tracking task. In this article, an effective observation model is proposed based on confidence (classification) score provided by the developing online prototypes based discriminative appearance model. This appearance model is constructed with the prior knowledge of two classes (object and background) and tries to discriminate between three classes such as object, background and occluded part of the object. The considered composite motion model can handle both the object motion as well as scale change of the object. The proposed update mechanism is able to adapt the appearance changes during tracking. We show a realization of the proposed method and demonstrate its performance (both quantitatively and qualitatively) with respect to state-of-the-art techniques on several challenging sequences. Analysis of the results concludes that the proposed technique can track (fully or partially) occluded objects as well as objects in various complex environments in a better way as compared to the existing ones.