Real-Time Tracking via Deformable Structure Regression Learning

Xian Yang, Quan Xiao, Shoujue Wang, Peizhong Liu

Abstract: Visual object tracking is a challenging task because designing an effective and efficient appearance model is difficult. Current online tracking algorithms treat tracking as a classification task and use labeled samples to update appearance model. However, it is not clear to evaluate instance confidence belong to the object. In this paper, we propose a simple and efficient tracking algorithm with a deformable structure appearance. In our method, model updates with continuous labeled samples which are dense sampling. In order to improve the accuracy, we introduce a couple-layer regression model which prevents negative background from impacting on the model learning rather than traditional classification. The proposed DSR tracker runs in real-time and performs favorably against state-of-the-art trackers on various challenging sequences.