Learning Discriminative Hidden Structural Parts for Visual Tracking

Abstract

Part-based visual tracking is attractive in recent years due to its robustness to occlusion and non-rigid motion. However, how to automatically generate the discriminative structural parts and consider their interactions jointly to construct a more robust tracker still remains unsolved. This paper proposes a discriminative structural part learning method while integrating the structure information, to address the visual tracking problem. Particulary, the state (e.g. position, width and height) of each part is regarded as a hidden variable and inferred automatically by considering the inner structure information of the target and the appearance difference between the target and the background. The inner structure information considering the relationship between neighboring parts, is integrated using a graph model based on a dynamically constructed pair-wise Markov Random Field. Finally, we adopt Metropolis-Hastings algorithm integrated with the online Support Vector Machine to complete the hidden variable inference task. The experimental results on various challenging sequences demonstrate the favorable performance of the proposed tracker over the state-of-the-art ones.

Geometric Hypergraph Learning for Visual Tracking

“…To better solve the shape deformation and partial occlusion issue, part based methods are gaining popularity in visual tracking. Wen et al [36] present a discriminative learning method to infer the position, shape and size of each part, using the Metropolis-Hastings algorithm integrated with an online SVM. Wang and Nevatia [35] propose to track non-rigid objects with multiple related parts and model tracking as Dynamic Bayesian Network, where the spatial relations among parts are formulated probabilistically.…”