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Conference Paper
Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning
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- Authors
- Sangdoo Yun, Jongwon Choi, Youngjoon Yoo, Kimin Yun, Jin Young Choi
- Issue Date
- 2017-07
- Citation
- Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pp.1349-1358
- Language
- English
- Type
- Conference Paper
- 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.
- KSP Keywords
- Competitive performance, Decision network, Deep reinforcement learning, Network-based, Partially labeled data, Pre-Training, Real-time, Reinforcement learning(RL), Semi-Supervised Learning(SSL), Target and background, Visual tracking