吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (11): 3429-3445.doi: 10.13229/j.cnki.jdxbgxb.20240149

• 综述 •    

基于深度学习的多目标跟踪研究进展综述

姜来为1(),王策2,杨宏宇1   

  1. 1.中国民航大学 安全科学与工程学院，天津 300300
    2.中国民航大学 计算机科学与技术学院，天津 300300
  • 收稿日期:2024-02-05 出版日期:2025-11-01 发布日期:2026-02-03
  • 作者简介:姜来为（1986-），女，讲师，博士. 研究方向：计算机视觉，无线通信，网络空间安全.E-mail： lwjiang@cauc.edu.cn
  • 基金资助:
    国家自然科学基金重点项目(U2033210);天津市教委科研计划项目(2020KJ025)

Review of multi-object tracking based on deep learning

Lai-wei JIANG1(),Ce WANG2,Hong-yu YANG1   

  1. 1.School of Safety Science and Engineering，Civil Aviation University of China，Tianjin 300300，China
    2.School of Computer Science and Technology，Civil Aviation University of China，Tianjin 300300，China
  • Received:2024-02-05 Online:2025-11-01 Published:2026-02-03

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摘要：

本文首先指出多目标跟踪任务面临的挑战和传统方法的局限性，其次对基于检测的跟踪和联合检测跟踪两类算法进行文献综述与分析，然后对多目标跟踪算法常用的评估指标和公开的数据集进行归纳，并对两类算法的性能进行比较分析，最后立足于当前研究现状对有待解决的问题和未来研究重点做出预测和展望。

关键词: 计算机视觉, 多目标跟踪, 基于检测的跟踪, 联合检测跟踪, 深度学习

Abstract:

Firstly， this paper points out the challenges faced by the design of multi-target tracking algorithms and the limitations of traditional methods. Secondly， a literature review and analysis of two types of algorithms are conducted： detection-based-tracking and joint-detection-tracking. Then， the commonly used evaluation indicators and publicly available datasets in the multi-object tracking algorithms were summarized， and the performance indicators of the two types of methods were analyzed. Finally， based on the current research status， the predictions and outlooks on the problems to be solved and the focuses of the future researches are made.

Key words: computer vision, multi-object tracking, detection based tracking, joint detection tracking, deep learning

中图分类号: 

  • TP391

图1

MOT常见干扰因素"

图2

不同阶段代表性MOT算法发展脉络图"

图3

DBT和JDT算法流程图"

图4

常见的基于深度学习的MOT方法"

图5

孪生网络目标跟踪框架"

表1

MOT典型数据集概况"

数据集名称年份视频序列大小/GB特点及描述
KITTI Tracking［68］20125015允许同时追踪人和车辆
MOT15［69］2015221.3场景丰富，密集程度低
MOT16［70］2016141.9场景丰富，规范标注，目标密集程度更高
MOT17［70］2016145.5增加更多场景，提供了更多的公开检测器
MOT20［71］202085.0人群更加密集，相互遮挡情况更加严重
DanceTrack［72］202210016.5目标外观相似且存在大量遮挡，运动模式复杂

表2

MOT17数据集算法性能评估结果"

方法类别检测器其他数据MOTA/%↑IDF1/%↑HOTA/%↑FN↓FP↓IDs↓速度/FPS
DeepSORT［33］DBT公共否60.357.4——36 1115 5298.1
DAN［25］DBT公共否53.949.5—234 59225 4238 4316.3
GHOST［35］DBT私有否78.777.162.8———2 325
SHSHI［40］DBT公共否62.071.554.6183 82529 4281 04121.1
ByteTrack［23］DBT公共是67.470.056.1172 6369 9391 33127.4
ByteTrack*［23］DBT私有是80.377.363.183 72125 4912 19629.6
MTA［30］DBT公共否67.169.2—161 54722 7561 27918.5
MAT［30］DBT私有否69.563.1—138 74130 6602 84418.5
OC-SORT*［29］DBT私有是78.077.563.2108 00015 1001 950—
MotionTrack［31］DBT私有是81.180.165.181 66023 8021 149—
TransTrack［42］JDT私有是74.563.954.1112 13728 3233 36310.0
TransMOT［46］JDT私有是76.775.161.793 15036 2312 3469.6
TransCenter［45］JDT私有否71.962.354.4137 00817 3784 0461.0
TrackFormer［43］JDT私有是74.168.057.3108 77734 6022 8927.4
MOTR［47］JDT私有否73.468.657.8135 56121 1232 1157.5
QDTrack［53］JDT私有否68.766.3—146 64326 5893 378—
SiamMOT［55］JDT公共是65.963.3—170 95518 0983 040—
MOTRv2［48］JDT私有否78.675.062.0————
JDE［57］JDT私有是63.059.5—162 92739 8886 17118.8
CStrack［61］JDT私有是74.972.3—114 30323 8473 56716.4
FairMOT［62］JDT私有是73.772.359.3117 47727 5073 30325.9
AdaMOT［64］JDT私有是74.975.5—112 13426 8832 61326.0
AdaMOT*［64］JDT私有是75.775.5—95 38539 7772 22626.0

表3

MOT20数据集算法性能评估结果"

方法类别检测器其他数据MOTA/%↑IDF1/%↑HOTA/%↑FN↓FP↓IDs↓速度/FPS
ByteTrack［23］DBT私有是77.875.261.387 59426 2491 22317.5
GHOST［35］DBT公共否52.755.343.4——1 216—
GHOST［35］DBT私有否73.775.261.2————
SHSHI［40］DBT公共否61.671.655.4168 09829 4291 0535.5
OC-SORT*［29］DBT私有否75.575.962.1108 00018 000913—
MotionTrack［31］DBT私有是78.076.562.884 15228 6291 165—
BASE［32］DBT私有是78.277.663.684 21127 60698416.8
CStrack［61］JDT私有是66.668.6—144 35825 4043 1964.5
FairMOT［62］JDT私有是61.867.354.688 901103 4401 33113.2
AdaMOT［64］JDT私有是68.871.8—117 00941 9932 40612.1
AdaMOT*［64］JDT私有是69.171.4—99 83358 4711 79212.1
TrackFormer［43］JDT私有是68.665.754.1140 37320 3841 5325.7
TransTrack［42］JDT私有是65.059.448.9150 19727 1913 06814.9
TransCenter［45］JDT私有是61.049.843.5147 89049 1894 4931.0
MOTRv2［48］JDT私有是76.272.260.3————

表 4

DanceTrack数据集算法性能评估结果"

方法类别检测器其他数据HOTA/%↑DetA/%↑AssA/%↑MOTA/%↑IDF1/%↑
ByteTrack*［23］DBT私有是47.771.032.189.653.9
OC-SORT*［29］DBT私有是55.180.440.492.254.9
SUSHI［40］DBT私有是63.380.150.188.763.4
Hybrid-SORT［36］DBT私有否62.263.047.481.991.6
FairMOT［62］JDT私有是39.766.723.882.240.8
CenterTrack［57］JDT私有否41.878.122.686.835.7
MOTR［47］JDT私有是54.273.540.279.751.5
MOTRv2［48］JDT私有是73.483.764.492.176.0
MeMOTR［49］JDT私有否68.580.558.489.971.2
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