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학술대회 Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning
Cited 87 time in scopus
저자
윤상두, 최종원, 유영준, 윤기민, 최진영
발행일
201707
출처
Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pp.1349-1358
DOI
https://dx.doi.org/10.1109/CVPR.2017.148
협약과제
17HS3600, (1세부) 실시간 대규모 영상 데이터 이해·예측을 위한 고성능 비주얼 디스커버리 플랫폼 개발, 박종열
초록
© 2017 IEEE. 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 제안 키워드
Competitive performance, Decision networks, Deep reinforcement learning, Online adaptation, Partially labeled data, Pre-Training, Real-Time, Reinforcement Learning(RL), Semi-Supervised Learning(SSL), Target and background, Visual Tracking