BoT-SORT: Robust Associations Multi-Pedestrian Tracking

TL;DR

BoT-SORT is a new multi-object tracking method that combines motion, appearance, and camera-motion compensation, achieving state-of-the-art results on MOTChallenge datasets.

Contribution

The paper introduces BoT-SORT, a robust multi-object tracker that integrates multiple cues and improves accuracy over existing methods.

Findings

Achieves top performance on MOT17 and MOT20 datasets.

Outperforms previous trackers in MOTA, IDF1, and HOTA metrics.

Provides open-source code and pre-trained models.

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