BoT-SORT: Robust Associations Multi-Pedestrian Tracking

Nir Aharon, Roy Orfaig, Ben-Zion Bobrovsky
6/29/2022
cs.CV

Official code: NirAharon/BoT-SORT (1,492 stars, MIT, PyTorch, last commit 2024-08-08).

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 https://github.com/NirAharon/BOT-SORT

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Code Implementations(1)

NirAharon/BoT-SORTOfficial100%
PyTorchMIT

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

1,492493C++, CMakeJul 1, 20222 years agoMIT
multi-object-trackermulti-object-trackingmulti-pedestrian-trackingpytorchtracking-by-detection+2 more

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Cite this paper

@article{aharon2022botsort,
  title  = {BoT-SORT: Robust Associations Multi-Pedestrian Tracking},
  author = {Nir Aharon and Roy Orfaig and Ben-Zion Bobrovsky},
  year   = {2022},
  eprint = {2206.14651},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2206.14651}
}

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