P2T: Part-to-Target Tracking via Deep Regression Learning.

Abstract

Most existing part based tracking methods are part-to-part trackers, which usually have two separated steps including part matching and target localization. Different from existing methods, in this paper, we propose a novel part-totarget (P2T) tracker in a unified fashion by inferring target location from parts directly. To achieve this goal, we propose a novel deep regression model for part to target regression in an end-to-end framework via Convolutional Neural Networks. The proposed model is able to not only exploit part context information to preserve object spatial layout structure, but also learn part reliability to emphasize part importance for robust part to target regression. We evaluate the proposed tracker on 4 challenging benchmark sequences, and extensive experimental results demonstrate that our method performs favorably against state-of-the-art trackers because of the powerful capacity of the proposed deep regression model.