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BoT-SORT: Robust Associations Multi-Pedestrian Tracking

2022-06-29 13:45:03
Nir Aharon, Roy Orfaig, Ben-Zion Bobrovsky
arXiv_CV
     
Abstract

The goal of multi-object tracking (MOT) is detecting and tracking all the objects in a scene, while keeping a unique identifier for each object. In this paper, we present a new robust state-of-the-art tracker, which can combine the advantages of motion and appearance information, along with camera-motion compensation, and a more accurate Kalman filter state vector. Our new trackers BoT-SORT, and BoT-SORT-ReID rank first in the datasets of MOTChallenge [29, 11] on both MOT17 and MOT20 test sets, in terms of all the main MOT metrics: MOTA, IDF1, and HOTA. For MOT17: 80.5 MOTA, 80.2 IDF1, and 65.0 HOTA are achieved. The source code and the pre-trained models are available at this https URL

Abstract (translated)

URL

https://arxiv.org/abs/2206.14651

PDF

https://arxiv.org/pdf/2206.14651.pdf

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