Maximum Entropy Reinforced Single Object Visual Tracking

Chenghuan Liu, Du Huynh, Mark Reynolds

Research output: Chapter in Book/Conference paperConference paperpeer-review


Single object visual tracking is a fundamental problem in computer vision and has many applications. Given only the location of the target of interest in the first video frame, a visual tracking algorithm must track the target until the end of the video while having to face challenging factors such as illumination change and scale variation. In this paper, we formulate this tracking problem in a framework of maximum entropy reinforcement learning where the agent is our visual tracker and the goal is to learn a tracking policy that maximises both the expected reward and its entropy so as to achieve a balance between exploitation and exploration. The aim of our tracking framework is to improve the tracking accuracy while giving the tracking agent the ability to avoid getting stuck on a nontarget object. Extensive experiments have been performed on a range of benchmarks where our method achieves state-of-the-art performance. Furthermore, we demonstrate that, in contrast to other visual trackers based on deep reinforcement learning, our method can run in real-time while maintaining high tracking accuracy.
Original languageEnglish
Title of host publicationECAI 2020: 24th European Conference on Artificial Intelligence 29 August–8 September 2020, Santiago de Compostela, SpainIncluding10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020) Proceedings
EditorsGiuseppe De Giacomo, Alejandro Catala, Bistra Dilkina, Michela Milano, Senen Barro, Alberto Bugarin, Jerome Lang
Place of PublicationNetherlands
PublisherIOS Press
Number of pages8
ISBN (Electronic)9781643681009
ISBN (Print)978-1-64368-100-9
Publication statusPublished - 24 Aug 2020
Event24th European Conference on Artificial Intelligence -
Duration: 29 Aug 20208 Sept 2020

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314


Conference24th European Conference on Artificial Intelligence
Abbreviated titleECAI 2020
Internet address


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