BoT-SORT: Robust Associations Multi-Pedestrian Tracking

By Nir Aharon, Roy Orfaig, Ben-Tzion Bobrovsky. arXiv.org, 2022

942 citations recorded by the source corpus.

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

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fbe974fc4dc9ca669c394b880a3969bf3d7955ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

Citations in the corpus

Deep OC-Sort: Multi-Pedestrian Tracking by Adaptive Re-Identification (2023, methodology): “For CMC, we adopt the OpenCV contrib VidStab module to generate similarity transforms using feature point extraction, optical ﬂow, and RANSAC, as previous works [3] choose.”
Zero-Shot Semantic Re-Identification for Autonomous Driving: A VLM Baseline Study (2026, unknown): “More recent MOT trackers such as BoT-SORT [3] combine motion and appearance cues with camera-motion compensation and an improved Kalman filter state vector, while StrongSORT [4] revisits the DeepSORT baseline and strengthens detection, feature embedding, and trajectory association with lightweight…”