Flow Guided Short-Term Trackers with Cascade Detection for Long-Term Tracking

Han Wu, Xueyuan Yang, Yong Yang, Guizhong Liu

ICCVW 2019 pp. 170-178

doi:10.1109/ICCVW.2019.00026 /iccvw/2019/wu2019iccvw-flow/

Abstract

Object tracking has been studied for decades, but most of the existing works are focused on the short-term tracking. For a long sequence, the object is often fully occluded or out of view for a long time, and existing short-term object tracking algorithms often lose the target, and it is difficult to re-catch the target even if it reappears again. In this paper a novel long-term object tracking algorithm flow_MDNet_RPN is proposed, in which a tracking result judgement module and a detection module are added to the short-term object tracking algorithm. Experiments show that the proposed long-term tracking algorithm is effective to the problem of target disappearance.

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Cite

Text

Wu et al. "Flow Guided Short-Term Trackers with Cascade Detection for Long-Term Tracking." IEEE/CVF International Conference on Computer Vision Workshops, 2019. doi:10.1109/ICCVW.2019.00026

Markdown

[Wu et al. "Flow Guided Short-Term Trackers with Cascade Detection for Long-Term Tracking." IEEE/CVF International Conference on Computer Vision Workshops, 2019.](https://mlanthology.org/iccvw/2019/wu2019iccvw-flow/) doi:10.1109/ICCVW.2019.00026

BibTeX

@inproceedings{wu2019iccvw-flow,
  title     = {{Flow Guided Short-Term Trackers with Cascade Detection for Long-Term Tracking}},
  author    = {Wu, Han and Yang, Xueyuan and Yang, Yong and Liu, Guizhong},
  booktitle = {IEEE/CVF International Conference on Computer Vision Workshops},
  year      = {2019},
  pages     = {170-178},
  doi       = {10.1109/ICCVW.2019.00026},
  url       = {https://mlanthology.org/iccvw/2019/wu2019iccvw-flow/}
}