Deep Activation Feature Maps for Visual Object Tracking

Abstract

Video object tracking is an important task with a broad range of applications. In this paper, we propose a novel visual tracking algorithm based on deep activation feature maps in correlation filter framework. Deep activation feature maps are generated from convolution neural network feature maps, which can discover the important part of the tracking target and overcome shape deformation and heavy occlusion. In addition, the scale variation is calculated by another correlation filter with histogram of oriented gradient (HoG) features. Moreover, we integrate the final tracking result in each frame based on the appearance model and scale model to further boost the overall tracking performance. We validate the effectiveness of our approach on a challenging benchmark, where the proposed method illustrates outstanding performance compared with the state-ofthe-art tracking algorithms