Action-Driven Visual Object Tracking With Deep Reinforcement Learning

IEEE Transactions on Neural Networks and Learning Systems
Sangdoo YunJin Young Choi

Abstract

In this paper, we propose an efficient visual tracker, which directly captures a bounding box containing the target object in a video by means of sequential actions learned using deep neural networks. The proposed deep neural network to control tracking actions is pretrained using various training video sequences and fine-tuned during actual tracking for online adaptation to a change of target and background. The pretraining is done by utilizing deep reinforcement learning (RL) as well as supervised learning. The use of RL enables even partially labeled data to be successfully utilized for semisupervised learning. Through the evaluation of the object tracking benchmark data set, the proposed tracker is validated to achieve a competitive performance at three times the speed of existing deep network-based trackers. The fast version of the proposed method, which operates in real time on graphics processing unit, outperforms the state-of-the-art real-time trackers with an accuracy improvement of more than 8%.

References

Dec 22, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·B BabenkoSerge Belongie
Dec 14, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Z KalalJiri Matas
Feb 27, 2015·Nature·Volodymyr MnihDemis Hassabis
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yi WuMing-Hsuan Yang
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·João F HenriquesJorge Batista
Jul 1, 2014·IEEE Transactions on Pattern Analysis and Machine Intelligence·Arnold W M SmeuldersMubarak Shah
Jan 29, 2016·Nature·David SilverDemis Hassabis
Feb 4, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Hanxi LiFatih Porikli

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Citations

Nov 17, 2020·Frontiers in Neurorobotics·Zhenshan BingAlois Knoll

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