Abstract

This paper proposes a novel tracker which is controlled by sequentially pursuing actions learned by deep reinforcement learning. In contrast to the existing trackers using deep networks, the proposed tracker is designed to achieve a light computation as well as satisfactory tracking accuracy in both location and scale. The deep network to control actions is pre-trained using various training sequences and fine-tuned during tracking for online adaptation to target and background changes. The pre-training is done by utilizing deep reinforcement learning as well as supervised learning. The use of reinforcement learning enables even partially labeled data to be successfully utilized for semi-supervised learning. Through evaluation of the OTB dataset, the proposed tracker is validated to achieve a competitive performance that is three times faster than state-of-the-art, deep network–based trackers. The fast version of the proposed method, which operates in real-time on GPU, outperforms the state-of-the-art real-time trackers.

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Citation

@InProceedings{yun2017adnet,

title={Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning},

author={Yun, Sangdoo and Choi, Jongwon and Yoo, Youngjoon and Yun, Kimin and Young Choi, Jin},

booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},

year={2017}

}