A dense connection based network for real-time object tracking

With the development of deep learning, the performance of many computer vision tasks has been greatly improved. For visual tracking, deep learning methods mainly focus on extracting better features or designing end-to-end trackers. However, during tracking specific targets most of the existing trackers based on deep learning are less discriminative and time-consuming. In this paper, a cascade based tracking algorithm is proposed to promote the robustness of the tracker and reduce time consumption. First, we propose a novel deep network for feature extraction. Since some pruning strategies are applied, the speed of the feature extraction stage can be more than 50 frames per second. Then, a cascade tracker named DCCT is presented to improve the performance and enhance the robustness by utilizing both texture and semantic features. Similar to the cascade classifier, the proposed DCCT tracker consists of several weaker trackers. Each weak tracker rejects some false candidates of the tracked object, and the final tracking results are obtained by synthesizing these weak trackers. Intensive experiments are conducted in some public datasets and the results have demonstrated the effectiveness of the proposed framework.