BoT-SORT: Robust Associations Multi-Pedestrian Tracking

29 Jun 2022  ·  Nir Aharon, Roy Orfaig, Ben-Zion Bobrovsky ·

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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Results from the Paper


 Ranked #1 on Multi-Object Tracking on MOT17 (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Benchmark
Multi-Object Tracking MOT17 BoT-SORT MOTA 80.5 # 1
IDF1 80.2 # 1
HOTA 65.0 # 1
Multi-Object Tracking MOT20 BoT-SORT MOTA 77.8 # 1
IDF1 77.5 # 1
HOTA 63.3 # 2

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