基于深度学习的多目标跟踪研究进展综述

doi:10.13229/j.cnki.jdxbgxb.20240149

Abstract

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

CLC Number:

TP391

Lai-wei JIANG,Ce WANG,Hong-yu YANG. Review of multi-object tracking based on deep learning[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(11): 3429-3445.

Figures/Tables 9

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References 72

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数据集名称	年份	视频序列	大小/GB	特点及描述
KITTI Tracking^［68］	2012	50	15	允许同时追踪人和车辆
MOT15^［69］	2015	22	1.3	场景丰富，密集程度低
MOT16^［70］	2016	14	1.9	场景丰富，规范标注，目标密集程度更高
MOT17^［70］	2016	14	5.5	增加更多场景，提供了更多的公开检测器
MOT20^［71］	2020	8	5.0	人群更加密集，相互遮挡情况更加严重
DanceTrack^［72］	2022	100	16.5	目标外观相似且存在大量遮挡，运动模式复杂

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	60.3	57.4	—	—	36 111	5 529	8.1
DAN^［25］	DBT	公共	否	53.9	49.5	—	234 592	25 423	8 431	6.3
GHOST^［35］	DBT	私有	否	78.7	77.1	62.8	—	—	—	2 325
SHSHI^［40］	DBT	公共	否	62.0	71.5	54.6	183 825	29 428	1 041	21.1
ByteTrack^［23］	DBT	公共	是	67.4	70.0	56.1	172 636	9 939	1 331	27.4
ByteTrack*^［23］	DBT	私有	是	80.3	77.3	63.1	83 721	25 491	2 196	29.6
MTA^［30］	DBT	公共	否	67.1	69.2	—	161 547	22 756	1 279	18.5
MAT^［30］	DBT	私有	否	69.5	63.1	—	138 741	30 660	2 844	18.5
OC-SORT*^［29］	DBT	私有	是	78.0	77.5	63.2	108 000	15 100	1 950	—
MotionTrack^［31］	DBT	私有	是	81.1	80.1	65.1	81 660	23 802	1 149	—
TransTrack^［42］	JDT	私有	是	74.5	63.9	54.1	112 137	28 323	3 363	10.0
TransMOT^［46］	JDT	私有	是	76.7	75.1	61.7	93 150	36 231	2 346	9.6
TransCenter^［45］	JDT	私有	否	71.9	62.3	54.4	137 008	17 378	4 046	1.0
TrackFormer^［43］	JDT	私有	是	74.1	68.0	57.3	108 777	34 602	2 892	7.4
MOTR^［47］	JDT	私有	否	73.4	68.6	57.8	135 561	21 123	2 115	7.5
QDTrack^［53］	JDT	私有	否	68.7	66.3	—	146 643	26 589	3 378	—
SiamMOT^［55］	JDT	公共	是	65.9	63.3	—	170 955	18 098	3 040	—
MOTRv2^［48］	JDT	私有	否	78.6	75.0	62.0	—	—	—	—
JDE^［57］	JDT	私有	是	63.0	59.5	—	162 927	39 888	6 171	18.8
CStrack^［61］	JDT	私有	是	74.9	72.3	—	114 303	23 847	3 567	16.4
FairMOT^［62］	JDT	私有	是	73.7	72.3	59.3	117 477	27 507	3 303	25.9
AdaMOT^［64］	JDT	私有	是	74.9	75.5	—	112 134	26 883	2 613	26.0
AdaMOT*^［64］	JDT	私有	是	75.7	75.5	—	95 385	39 777	2 226	26.0

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	77.8	75.2	61.3	87 594	26 249	1 223	17.5
GHOST^［35］	DBT	公共	否	52.7	55.3	43.4	—	—	1 216	—
GHOST^［35］	DBT	私有	否	73.7	75.2	61.2	—	—	—	—
SHSHI^［40］	DBT	公共	否	61.6	71.6	55.4	168 098	29 429	1 053	5.5
OC-SORT*^［29］	DBT	私有	否	75.5	75.9	62.1	108 000	18 000	913	—
MotionTrack^［31］	DBT	私有	是	78.0	76.5	62.8	84 152	28 629	1 165	—
BASE^［32］	DBT	私有	是	78.2	77.6	63.6	84 211	27 606	984	16.8
CStrack^［61］	JDT	私有	是	66.6	68.6	—	144 358	25 404	3 196	4.5
FairMOT^［62］	JDT	私有	是	61.8	67.3	54.6	88 901	103 440	1 331	13.2
AdaMOT^［64］	JDT	私有	是	68.8	71.8	—	117 009	41 993	2 406	12.1
AdaMOT*^［64］	JDT	私有	是	69.1	71.4	—	99 833	58 471	1 792	12.1
TrackFormer^［43］	JDT	私有	是	68.6	65.7	54.1	140 373	20 384	1 532	5.7
TransTrack^［42］	JDT	私有	是	65.0	59.4	48.9	150 197	27 191	3 068	14.9
TransCenter^［45］	JDT	私有	是	61.0	49.8	43.5	147 890	49 189	4 493	1.0
MOTRv2^［48］	JDT	私有	是	76.2	72.2	60.3	—	—	—	—

方法	类别	检测器	其他数据	HOTA/%↑	DetA/%↑	AssA/%↑	MOTA/%↑	IDF₁/%↑
ByteTrack*^［23］	DBT	私有	是	47.7	71.0	32.1	89.6	53.9
OC-SORT*^［29］	DBT	私有	是	55.1	80.4	40.4	92.2	54.9
SUSHI^［40］	DBT	私有	是	63.3	80.1	50.1	88.7	63.4
Hybrid-SORT^［36］	DBT	私有	否	62.2	63.0	47.4	81.9	91.6
FairMOT^［62］	JDT	私有	是	39.7	66.7	23.8	82.2	40.8
CenterTrack^［57］	JDT	私有	否	41.8	78.1	22.6	86.8	35.7
MOTR^［47］	JDT	私有	是	54.2	73.5	40.2	79.7	51.5
MOTRv2^［48］	JDT	私有	是	73.4	83.7	64.4	92.1	76.0
MeMOTR^［49］	JDT	私有	否	68.5	80.5	58.4	89.9	71.2

Review of multi-object tracking based on deep learning

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