From certain to uncertain: Toward optimal solution for offline multiple object tracking

Kaikai Zhao, Takashi Imaseki, Hiroshi Mouri, Einoshin Suzuki, Tetsu Matsukawa

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Affinity measure in object tracking outputs a similarity or distance score for given detections. As an affinity measure is typically imperfect, it generally has an uncertain region in which regarding two groups of detections as the same object or different objects based on the score can be wrong. How to reduce the uncertain region is a major challenge for most similarity-based tracking methods. Early mistakes often result in distribution drifts for tracked objects and this is another major issue for object tracking. In this paper, we propose a new offline tracking method called agglomerative hierarchical clustering with ensemble of tracking experts (AHC_ETE), to tackle the uncertain region and early mistake issues. We conduct tracking from certain to uncertain to reduce early mistakes. Meanwhile, we ensemble multiple tracking experts to reduce the uncertain region as the final uncertain region is the intersection of those of all tracking experts. Experiments on the MOT15 and MOT16 datasets demonstrated the effectiveness of our method. The code is publicly available at https://github.com/cyoukaikai/ahc_ete.

本文言語英語
ホスト出版物のタイトルProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2506-2513
ページ数8
ISBN(電子版)9781728188089
DOI
出版ステータス出版済み - 2020
イベント25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, イタリア
継続期間: 1 10 20211 15 2021

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
ISSN(印刷版)1051-4651

会議

会議25th International Conference on Pattern Recognition, ICPR 2020
国/地域イタリア
CityVirtual, Milan
Period1/10/211/15/21

All Science Journal Classification (ASJC) codes

  • コンピュータ ビジョンおよびパターン認識

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