Real-Time Object Tracking with Generalized Part-Based Appearance Model and Structure-Constrained Motion Model

Abstract

In this paper, we propose a real-time object tracking approach. It utilizes generalized part-based appearance model and structure-constrained motion model as auxiliary. The appearance of the target object is modeled by the proposed generalized part-based appearance model, which combines the appearance of different parts of the target object, adaptively updated by an efficient structure learning scheme based on the online Passive-Aggressive algorithm. By integrating the confidence scores of multiple parts, mutual compensation is realized, significantly enhances the robustness of our method against the structure deformation and partial occlusion during the tracking. In addition, we enhance the performance of our tracker by using a motion model. It employs a structure-constrained rule, that is, the change on the structure of the target object between consecutive frames is small. Experiments on public video sequences verify the superior performance of our algorithm.