BoT-SORT: Robust Associations Multi-Pedestrian Tracking

Code

• github.com/niraharon/bot-sort
  OfficialIn paperpytorch★ 1,379
• github.com/mikel-brostrom/boxmot
  pytorch★ 8,068
• github.com/viplix3/BoTSORT-cpp
  none★ 226
• github.com/Robotmurlock/Motrack
  none★ 16
• github.com/airotau/pointpillarshailoinnoviz
  pytorch★ 7
• github.com/tensorworksio/mot.cpp
  none★ 3
• github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/mot
  paddle★ 0

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

Tasks

Benchmark Results

Reproductions