Context-aware deep feature compression for high-speed visual tracking

Research output: Chapter in Book/Report/Conference proceedingConference contribution


  • Jongwon Choi
  • Tobias Fischer
  • Sangdoo Yun
  • Kyuewang Lee
  • Jiyeoup Jeong
  • Yiannis Demiris
  • Jin Young Choi

Colleges, School and Institutes

External organisations

  • Clova AI Research, NAVER Corp.
  • Seoul National University
  • Imperial College London


We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers. The major contribution to the high computational speed lies in the proposed deep feature compression that is achieved by a context-aware scheme utilizing multiple expert auto-encoders; a context in our framework refers to the coarse category of the tracking target according to appearance patterns. In the pre-training phase, one expert auto-encoder is trained per category. In the tracking phase, the best expert auto-encoder is selected for a given target, and only this auto-encoder is used. To achieve high tracking performance with the compressed feature map, we introduce extrinsic denoising processes and a new orthogonality loss term for pre-training and fine-tuning of the expert autoencoders. We validate the proposed context-aware framework through a number of experiments, where our method achieves a comparable performance to state-of-the-art trackers which cannot run in real-time, while running at a significantly fast speed of over 100 fps.


Original languageEnglish
Title of host publicationProceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018)
Publication statusPublished - 17 Dec 2018
EventIEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018) - Salt Lake City, United States
Duration: 18 Jun 201822 Jun 2018

Publication series

NameProceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition)
ISSN (Electronic)2575-7075


ConferenceIEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018)
CountryUnited States
CitySalt Lake City