Multi-Object Tracking with Siamese Track-RCNN

• URL: http://arxiv.org/abs/2004.07786v1
• Date: Thu, 16 Apr 2020 17:28:52 GMT
• Title: Multi-Object Tracking with Siamese Track-RCNN
• Authors: Bing Shuai, Andrew G. Berneshawi, Davide Modolo, Joseph Tighe
• Abstract summary: Siamese Track-RCNN is a two stage detect-and-track framework which consists of three functional branches. Siamese Track-RCNN achieves significantly higher results than the state-of-the-art, while also being much more efficient, thanks to its unified design.
• Score: 23.753970697176946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-object tracking systems often consist of a combination of a detector, a short term linker, a re-identification feature extractor and a solver that takes the output from these separate components and makes a final prediction. Differently, this work aims to unify all these in a single tracking system. Towards this, we propose Siamese Track-RCNN, a two stage detect-and-track framework which consists of three functional branches: (1) the detection branch localizes object instances; (2) the Siamese-based track branch estimates the object motion and (3) the object re-identification branch re-activates the previously terminated tracks when they re-emerge. We test our tracking system on two popular datasets of the MOTChallenge. Siamese Track-RCNN achieves significantly higher results than the state-of-the-art, while also being much more efficient, thanks to its unified design.

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