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Papers/BoT-SORT: Robust Associations Multi-Pedestrian Tracking

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

Nir Aharon, Roy Orfaig, Ben-Zion Bobrovsky

2022-06-29Multi-Object TrackingObject Tracking
PaperPDFCodeCodeCodeCodeCodeCode(official)Code

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

Results

TaskDatasetMetricValueModel
Multi-Object TrackingMOT20HOTA63.3BoT-SORT
Multi-Object TrackingMOT20IDF177.5BoT-SORT
Multi-Object TrackingMOT20MOTA77.8BoT-SORT
Multi-Object TrackingMOT17HOTA65BoT-SORT
Multi-Object TrackingMOT17IDF180.2BoT-SORT
Multi-Object TrackingMOT17MOTA80.5BoT-SORT
Object TrackingQuadTrackHOTA15.77Bot-SORT
Object TrackingMOT20HOTA63.3BoT-SORT
Object TrackingMOT20IDF177.5BoT-SORT
Object TrackingMOT20MOTA77.8BoT-SORT
Object TrackingMOT17HOTA65BoT-SORT
Object TrackingMOT17IDF180.2BoT-SORT
Object TrackingMOT17MOTA80.5BoT-SORT

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