Context-Aware Correlation Filter for Visual Tracking with Deep Convolution Features

Abstract

Visual object tracking is a challenging problem due to appearance variation of target. Correlation filter (CF) -based trackers have shown competing results for visual object tracking. However, they perform poorly in the case of abrupt motion and heavy background clutter due to less use of contextual information. In this paper, we solve this problem by explicitly incorporating contextual information into a context-aware (CA) framework. Under this framework, deep features from higher convolutional layers encode more semantic information of target which are robust to appearance variations, and features from lower layers locate the target more precise. Compared with handcrafted features, DL-based representation learning require less human interventions and provide much better performance. Extensive experimental results on largescale benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art methods.