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2020

ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation

Yang, Fan, Chang, Xin, Dang, Chenyu et al.

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We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement.

However, it remains challenging for refining MOTS results, which could be attributed to that appearance features are not adapted to target videos and it is also difficult to find proper thresholds to discriminate them.
To tackle this issue, we propose a self-supervised refining MOTS (i.e., ReMOTS) framework.
ReMOTS mainly takes four steps to refine MOTS results from the data association perspective.

ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation · Around

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Detectron2
Original
Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick · 2007
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