BoT-SORT: Robust Associations Multi-Pedestrian Tracking
This improves multi-pedestrian tracking for applications like surveillance and autonomous driving, but it is incremental as it builds on existing tracking methods.
The paper tackles multi-object tracking by introducing BoT-SORT, a tracker that combines motion and appearance information with camera-motion compensation and an improved Kalman filter, achieving state-of-the-art results with 80.5 MOTA, 80.2 IDF1, and 65.0 HOTA on MOT17.
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