Method
Setting
Code
HOTA
DetA
AssA
DetRe
DetPr
AssRe
AssPr
LocA
MOTA
1
HRI-SFMOT
83.04 %
79.87 %
87.15 %
85.17 %
85.65 %
89.91 %
91.74 %
87.99 %
92.62 %
2
BiTrack
82.69 %
80.13 %
86.07 %
84.71 %
87.08 %
89.03 %
92.11 %
88.65 %
91.65 %
3
IMOU_ALG
82.08 %
78.78 %
86.21 %
84.83 %
83.98 %
90.00 %
89.84 %
87.14 %
92.75 %
4
CrossTracker
82.00 %
79.96 %
84.75 %
83.48 %
87.92 %
87.20 %
92.12 %
88.39 %
91.97 %
5
VirConvTrack
code
81.87 %
78.14 %
86.39 %
82.00 %
86.92 %
89.08 %
91.58 %
88.04 %
90.24 %
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal
3D Object Detection . CVPR 2023.
6
STMOT
81.28 %
78.44 %
84.82 %
81.60 %
87.85 %
87.51 %
91.70 %
88.14 %
89.83 %
7
CasTrack
code
81.00 %
78.58 %
84.22 %
84.10 %
84.86 %
87.55 %
90.47 %
87.49 %
91.91 %
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for
3D Object Detection from LiDAR point clouds . IEEE TGRS 2022. H. Wu, W. Han, C. Wen, X. Li and C. Wang: 3D Multi-Object Tracking in Point Clouds
Based on Prediction Confidence-Guided Data
Association . IEEE TITS 2021.
8
PC-TCNN
80.90 %
78.46 %
84.13 %
84.22 %
84.58 %
87.46 %
90.47 %
87.48 %
91.70 %
H. Wu, Q. Li, C. Wen, X. Li, X. Fan and C. Wang: Tracklet Proposal Network for Multi-Object
Tracking on Point Clouds . IJCAI 2021.
9
LEGO
80.75 %
78.91 %
83.27 %
84.64 %
84.94 %
86.87 %
90.19 %
87.92 %
90.61 %
Z. Zhang, J. Liu, Y. Xia, T. Huang, Q. Han and H. Liu: LEGO: Learning and Graph-Optimized Modular
Tracker for Online Multi-Object Tracking with Point
Clouds . arXiv preprint arXiv:2308.09908 2023.
10
StableMOT
80.68 %
77.57 %
84.70 %
82.73 %
85.69 %
87.67 %
91.54 %
88.27 %
88.91 %
11
STMOT_PointRCNN
80.43 %
78.10 %
83.52 %
83.52 %
84.93 %
86.80 %
90.82 %
87.58 %
90.34 %
12
Rethink MOT
80.39 %
77.88 %
83.64 %
84.23 %
83.57 %
87.63 %
88.90 %
87.07 %
91.53 %
L. Wang, J. Zhang, P. Cai and X. Li: Towards Robust Reference System for
Autonomous Driving: Rethinking 3D MOT . Proceedings of the 2023 IEEE
International Conference on Robotics and Automation
(ICRA) 2023.
13
CollabMOT-RAM
80.02 %
78.85 %
81.86 %
82.60 %
86.32 %
85.34 %
88.41 %
87.14 %
91.70 %
14
SSL3DMOT
79.64 %
76.36 %
83.68 %
79.36 %
88.38 %
86.26 %
91.86 %
88.62 %
87.29 %
15
RAM
79.53 %
78.79 %
80.94 %
82.54 %
86.33 %
84.21 %
88.77 %
87.15 %
91.61 %
P. Tokmakov, A. Jabri, J. Li and A. Gaidon: Object Permanence Emerges in a Random Walk along
Memory . ICML 2022.
16
UG3DMOT
code
78.60 %
76.01 %
82.28 %
80.77 %
85.44 %
85.36 %
91.37 %
87.84 %
87.98 %
J. He, C. Fu and X. Wang: 3D Multi-Object Tracking Based on
Uncertainty-Guided Data Association . arXiv preprint arXiv:2303.01786 2023.
17
CollabMOT-Permatrack
78.54 %
78.43 %
79.29 %
81.86 %
86.53 %
82.31 %
89.16 %
87.10 %
91.55 %
18
MSA-MOT
78.52 %
75.19 %
82.56 %
82.42 %
82.21 %
85.21 %
90.16 %
87.00 %
88.01 %
Z. Zhu, J. Nie, H. Wu, Z. He and M. Gao: MSA-MOT: Multi-Stage Association for 3D
Multimodality Multi-Object Tracking . Sensors 2022.
19
YONTD-MOT
code
78.08 %
74.16 %
82.86 %
78.95 %
85.71 %
85.44 %
91.74 %
88.23 %
85.09 %
X. Wang, J. He, C. Fu, T. Meng and M. Huang: You Only Need Two Detectors to Achieve
Multi-Modal 3D Multi-Object Tracking . arXiv preprint arXiv:2304.08709 2023.
20
PermaTrack
78.03 %
78.29 %
78.41 %
81.71 %
86.54 %
81.14 %
89.49 %
87.10 %
91.33 %
P. Tokmakov, J. Li, W. Burgard and A. Gaidon: Learning to Track with Object Permanence . ICCV 2021.
21
PC3T
code
77.80 %
74.57 %
81.59 %
79.19 %
84.07 %
84.77 %
88.75 %
86.07 %
88.81 %
H. Wu, W. Han, C. Wen, X. Li and C. Wang: 3D Multi-Object Tracking in Point Clouds
Based on Prediction Confidence-Guided Data
Association . IEEE TITS 2021.
22
Y3D
code
77.32 %
75.08 %
80.32 %
79.77 %
85.55 %
83.16 %
91.01 %
87.90 %
86.33 %
ERROR: Wrong syntax in BIBTEX file.
23
Stereo3DMOT
77.32 %
73.43 %
81.86 %
77.34 %
85.38 %
84.66 %
89.61 %
86.72 %
87.10 %
24
Loc Phenikaa-X
77.32 %
75.08 %
80.32 %
79.77 %
85.55 %
83.16 %
91.01 %
87.90 %
86.33 %
25
OC-SORT
code
76.54 %
77.25 %
76.39 %
80.64 %
86.36 %
80.33 %
87.17 %
87.01 %
90.28 %
J. Cao, X. Weng, R. Khirodkar, J. Pang and K. Kitani: Observation-Centric SORT: Rethinking SORT for Robust
Multi-Object Tracking . 2022.
26
S3Track
76.52 %
77.50 %
76.26 %
81.29 %
85.88 %
79.42 %
88.61 %
86.95 %
90.31 %
Anonymous: S$^3$Track: Self-supervised Tracking with
Soft Assignment Flow . .
27
Smart3DMOT
76.20 %
72.69 %
80.46 %
75.72 %
86.02 %
83.23 %
89.50 %
86.58 %
85.83 %
28
Anonymous
75.65 %
71.92 %
80.02 %
77.31 %
83.33 %
83.73 %
88.39 %
86.62 %
85.03 %
29
StrongFusion-MOT
75.65 %
72.08 %
79.84 %
75.20 %
86.23 %
82.42 %
89.81 %
86.74 %
85.53 %
X. Wang, C. Fu, J. He, S. Wang and J. Wang: StrongFusionMOT: A Multi-Object Tracking
Method Based on LiDAR-Camera Fusion . IEEE Sensors Journal 2022.
30
Mono_3D_KF
75.47 %
74.10 %
77.63 %
78.86 %
82.98 %
80.23 %
88.88 %
85.48 %
88.48 %
A. Reich and H. Wuensche: Monocular 3D Multi-Object Tracking with an EKF Approach for Long-Term Stable Tracks . 2021 IEEE 24th International Conference on Information Fusion (FUSION) 2021.
31
DeepFusion-MOT
code
75.46 %
71.54 %
80.05 %
75.34 %
85.25 %
82.63 %
89.77 %
86.70 %
84.63 %
X. Wang, C. Fu, Z. Li, Y. Lai and J. He: DeepFusionMOT: A 3D Multi-Object
Tracking Framework Based on Camera-LiDAR Fusion with
Deep Association . IEEE Robotics and Automation
Letters 2022.
32
CollabMOT-CenterTr
75.26 %
75.46 %
75.74 %
80.06 %
84.36 %
78.75 %
88.53 %
86.44 %
89.08 %
33
PolarMOT
code
75.16 %
73.94 %
76.95 %
80.81 %
82.40 %
80.00 %
89.27 %
87.12 %
85.08 %
A. Kim, G. Bras'o, A. O\vsep and L. Leal-Taix'e: PolarMOT: How Far Can Geometric Relations Take Us in 3D Multi-Object Tracking? . European Conference on Computer Vision (ECCV) 2022.
34
SG-AM
74.64 %
72.36 %
77.62 %
75.33 %
86.06 %
80.22 %
89.70 %
86.59 %
85.29 %
35
P3DTrack
74.59 %
72.88 %
76.86 %
78.09 %
83.21 %
80.66 %
86.67 %
86.28 %
85.60 %
36
EagerMOT
code
74.39 %
75.27 %
74.16 %
78.77 %
86.42 %
76.24 %
91.05 %
87.17 %
87.82 %
A. Kim, A. Osep and L. Leal-Taix'e: EagerMOT: 3D Multi-Object Tracking via Sensor Fusion . IEEE International Conference on Robotics and Automation (ICRA) 2021.
37
DEFT
code
74.23 %
75.33 %
73.79 %
79.96 %
83.97 %
78.30 %
85.19 %
86.14 %
88.38 %
M. Chaabane, P. Zhang, R. Beveridge and S. O'Hara: DEFT: Detection Embeddings for Tracking . Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR) Workshops 2021.
38
Rt_Track
74.00 %
71.90 %
77.11 %
75.50 %
83.38 %
80.68 %
86.32 %
84.91 %
86.67 %
39
SUAMOT
73.78 %
69.71 %
78.52 %
72.27 %
86.89 %
80.74 %
90.11 %
86.91 %
82.36 %
40
TripletTrack
73.58 %
73.18 %
74.66 %
76.18 %
86.81 %
77.31 %
89.55 %
87.37 %
84.32 %
N. Marinello, M. Proesmans and L. Van Gool: TripletTrack: 3D Object Tracking Using Triplet
Embeddings and LSTM . Proceedings of the IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR) Workshops 2022.
41
MCMOT CenterTrack
73.39 %
74.85 %
72.58 %
80.19 %
83.11 %
75.56 %
87.93 %
86.15 %
87.92 %
42
FFMOT
73.38 %
74.93 %
72.54 %
81.13 %
84.17 %
75.84 %
90.54 %
88.11 %
85.75 %
43
FNC2
73.19 %
73.27 %
73.77 %
80.98 %
81.67 %
77.05 %
89.84 %
87.31 %
84.21 %
H. Chao Jiang: A Fast and High-Performance Object Proposal
Method for Vision Sensors: Application to Object
Detection . IEEE sensors journal 2022.
44
mono3DT
code
73.16 %
72.73 %
74.18 %
76.51 %
85.28 %
77.18 %
87.77 %
86.88 %
84.28 %
H. Hu, Q. Cai, D. Wang, J. Lin, M. Sun, P. Krähenbühl, T. Darrell and F. Yu: Joint Monocular 3D Vehicle Detection and
Tracking . ICCV 2019.
45
LGM
73.14 %
74.61 %
72.31 %
80.53 %
82.16 %
76.38 %
84.74 %
85.85 %
87.60 %
G. Wang, R. Gu, Z. Liu, W. Hu, M. Song and J. Hwang: Track without Appearance: Learn Box and
Tracklet Embedding with Local and Global Motion
Patterns for Vehicle Tracking . Proceedings of the IEEE/CVF
International Conference on Computer Vision
(ICCV) 2021.
46
CenterTrack
code
73.02 %
75.62 %
71.20 %
80.10 %
84.56 %
73.84 %
89.00 %
86.52 %
88.83 %
X. Zhou, V. Koltun and P. Krähenbühl: Tracking Objects as Points . ECCV 2020.
47
QD-3DT
code
72.77 %
74.09 %
72.19 %
78.13 %
85.48 %
74.87 %
89.21 %
87.16 %
85.94 %
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking . ArXiv:2103.07351 2021.
48
TrackMPNN
code
72.30 %
74.69 %
70.63 %
80.02 %
83.11 %
73.58 %
87.14 %
86.14 %
87.33 %
A. Rangesh, P. Maheshwari, M. Gebre, S. Mhatre, V. Ramezani and M. Trivedi: TrackMPNN: A Message Passing Graph Neural
Architecture for Multi-Object Tracking . arXiv preprint arXiv:2101.04206 .
49
EAFFMOT
72.28 %
71.97 %
73.08 %
77.05 %
83.77 %
76.39 %
88.66 %
86.73 %
84.77 %
50
DiTMOT
code
72.21 %
71.09 %
74.04 %
75.98 %
83.28 %
76.57 %
89.97 %
86.15 %
84.53 %
S. Wang, P. Cai, L. Wang and M. Liu: DiTNet: End-to-End 3D Object Detection
and Track ID Assignment in Spatio-Temporal
World . IEEE Robotics and Automation Letters 2021.
51
SMAT
71.88 %
72.13 %
72.13 %
74.43 %
87.33 %
74.77 %
88.30 %
87.19 %
83.64 %
N. Gonzalez, A. Ospina and P. Calvez: SMAT: Smart Multiple Affinity Metrics for
Multiple Object Tracking . Image Analysis and Recognition 2020.
52
NC2
71.85 %
69.61 %
74.81 %
81.19 %
76.99 %
78.57 %
89.33 %
87.30 %
78.52 %
Chao Jiang and W. Zhiling: A New Adaptive Noise Covariance Matrices Estimation and Filtering Method: Application to Multi-Object Tracking . arXiv 2021.
53
CJMODT-v2
71.75 %
73.16 %
71.01 %
75.78 %
86.84 %
73.04 %
90.39 %
86.97 %
84.86 %
54
TuSimple
71.55 %
72.62 %
71.11 %
76.78 %
83.84 %
74.51 %
86.26 %
85.72 %
86.31 %
W. Choi: Near-online multi-target tracking with aggregated local flow
descriptor . Proceedings of the IEEE International Conference on
Computer Vision 2015. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition . Proceedings of the IEEE conference on computer vision
and pattern recognition 2016.
55
CJMODT-v3
71.09 %
73.19 %
69.63 %
75.81 %
86.95 %
71.73 %
90.06 %
87.13 %
84.75 %
56
BcMODT
71.00 %
73.62 %
69.14 %
78.86 %
83.97 %
72.34 %
88.70 %
86.93 %
85.48 %
K. Zhang, Y. Liu, F. Mei, J. Jin and Y. Wang: Boost Correlation Features with 3D-MiIoU-
Based Camera-LiDAR Fusion for MODT in Autonomous
Driving . Remote Sensing 2023.
57
AIPT
70.98 %
72.91 %
69.54 %
75.40 %
86.59 %
71.51 %
89.79 %
86.70 %
85.69 %
58
JMODT
code
70.73 %
73.45 %
68.76 %
78.67 %
84.02 %
72.46 %
88.02 %
86.95 %
85.35 %
K. Huang and Q. Hao: Joint multi-object detection and tracking
with camera-LiDAR fusion for autonomous driving . 2021 IEEE/RSJ International Conference
on Intelligent Robots and Systems (IROS) 2021.
59
3DMAETracking
70.45 %
70.76 %
70.70 %
74.19 %
85.34 %
73.73 %
87.64 %
86.70 %
82.72 %
60
AB3DMOT+PointRCNN
code
69.99 %
71.13 %
69.33 %
75.66 %
84.40 %
72.31 %
89.02 %
86.85 %
83.61 %
X. Weng, J. Wang, D. Held and K. Kitani: 3D Multi-Object Tracking: A Baseline and
New Evaluation Metrics . IROS 2020.
61
JRMOT
code
69.61 %
73.05 %
66.89 %
76.95 %
85.07 %
69.18 %
88.95 %
86.72 %
85.10 %
A. Shenoi, M. Patel, J. Gwak, P. Goebel, A. Sadeghian, H. Rezatofighi, R. Mart\'in-Mart\'in and S. Savarese: JRMOT: A Real-Time 3D Multi-Object
Tracker and a New Large-Scale Dataset . The IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS) 2020.
62
MOTSFusion
code
68.74 %
72.19 %
66.16 %
76.05 %
84.88 %
69.57 %
85.49 %
86.56 %
84.24 %
J. Luiten, T. Fischer and B. Leibe: Track to Reconstruct and Reconstruct to
Track . IEEE Robotics and Automation Letters 2020.
63
IMMDP
68.66 %
68.02 %
69.76 %
71.47 %
83.28 %
74.50 %
82.02 %
84.80 %
82.75 %
Y. Xiang, A. Alahi and S. Savarese: Learning to Track: Online Multi-
Object Tracking by Decision Making . International Conference on
Computer Vision (ICCV) 2015. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real-
Time
Object Detection with Region Proposal
Networks . NIPS 2015.
64
SRK_ODESA(hc)
68.51 %
75.40 %
63.08 %
78.89 %
86.00 %
65.89 %
87.47 %
86.88 %
87.79 %
D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking . ACCV 2020.
65
Quasi-Dense
code
68.45 %
72.44 %
65.49 %
76.01 %
85.37 %
68.28 %
88.53 %
86.50 %
84.93 %
J. Pang, L. Qiu, X. Li, H. Chen, Q. Li, T. Darrell and F. Yu: Quasi-Dense Similarity Learning for Multiple Object Tracking . CVPR 2021.
66
MASS
68.25 %
72.92 %
64.46 %
76.83 %
85.14 %
72.12 %
81.46 %
86.80 %
84.64 %
H. Karunasekera, H. Wang and H. Zhang: Multiple Object Tracking with attention to
Appearance, Structure, Motion and Size . IEEE Access 2019.
67
JCSTD
65.94 %
65.37 %
67.03 %
68.49 %
82.42 %
71.02 %
82.25 %
84.03 %
80.24 %
W. Tian, M. Lauer and L. Chen: Online Multi-Object Tracking Using Joint
Domain Information in Traffic Scenarios . IEEE Transactions on Intelligent
Transportation Systems 2019.
68
MC_CATrack
65.86 %
62.89 %
69.88 %
65.84 %
80.28 %
73.23 %
84.52 %
82.52 %
78.48 %
69
MDP
code
64.79 %
63.04 %
67.05 %
66.18 %
82.22 %
69.61 %
85.61 %
84.24 %
76.08 %
Y. Xiang, A. Alahi and S. Savarese: Learning to Track: Online Multi-
Object Tracking by Decision Making . International Conference on
Computer Vision (ICCV) 2015. Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural
Networks for Object Proposals and Detection . IEEE Winter Conference on Applications of
Computer Vision (WACV) 2017.
70
NOMT*
64.77 %
63.08 %
67.04 %
66.92 %
79.28 %
70.38 %
83.14 %
82.22 %
77.91 %
W. Choi: Near-Online Multi-target Tracking with
Aggregated Local Flow Descriptor
. ICCV 2015.
71
SRK_ODESA(mc)
64.25 %
74.87 %
55.70 %
78.62 %
84.68 %
62.10 %
81.78 %
85.85 %
88.50 %
D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking . ACCV 2020.
72
MOTBeyondPixels
code
63.75 %
72.87 %
56.40 %
76.58 %
85.38 %
59.05 %
86.70 %
86.90 %
82.68 %
S. Sharma, J. Ansari, J. Krishna Murthy and K. Madhava Krishna: Beyond Pixels: Leveraging
Geometry and
Shape Cues for Online Multi-Object
Tracking . Proceedings of the IEEE
International Conference on Robotics and
Automation (ICRA) 2018.
73
mmMOT
code
62.05 %
72.29 %
54.02 %
76.17 %
84.89 %
58.98 %
82.40 %
86.58 %
83.23 %
W. Zhang, H. Zhou, Sun, Z. Wang, J. Shi and C. Loy: Robust Multi-Modality Multi-Object
Tracking . International Conference on
Computer Vision (ICCV) 2019.
74
FANTrack
code
60.85 %
64.36 %
58.69 %
69.17 %
80.82 %
60.78 %
88.94 %
84.72 %
75.84 %
E. Baser, V. Balasubramanian, P. Bhattacharyya and K. Czarnecki: FANTrack: 3D Multi-Object Tracking with
Feature Association Network . ArXiv 2019.
75
DSM
60.05 %
64.09 %
57.18 %
67.22 %
83.64 %
59.91 %
86.32 %
85.39 %
73.94 %
D. Frossard and R. Urtasun: End-To-End Learning of Multi-Sensor 3D Tracking by Detection . ICRA 2018.
76
aUToTrack
59.83 %
67.82 %
53.68 %
72.66 %
79.60 %
55.94 %
86.52 %
83.10 %
80.97 %
K. Burnett, S. Samavi, S. Waslander, T. Barfoot and A. Schoellig: aUToTrack: A Lightweight Object Detection and Tracking System for
the SAE AutoDrive Challenge . arXiv:1905.08758 2019.
77
extraCK
59.76 %
65.18 %
55.47 %
69.21 %
81.69 %
61.82 %
75.70 %
84.30 %
79.29 %
G. Gunduz and T. Acarman: A lightweight online multiple object
vehicle tracking method . Intelligent Vehicles Symposium
(IV), 2018 IEEE 2018.
78
3D-CNN/PMBM
59.12 %
65.43 %
54.28 %
69.87 %
80.68 %
57.28 %
83.89 %
83.94 %
79.23 %
S. Scheidegger, J. Benjaminsson, E. Rosenberg, A. Krishnan and K. Granström: Mono-Camera 3D Multi-Object Tracking
Using Deep Learning Detections
and PMBM Filtering . 2018 IEEE Intelligent Vehicles
Symposium, IV 2018, Changshu, Suzhou,
China, June 26-30, 2018 2018.
79
NOMT-HM*
59.08 %
61.27 %
57.45 %
65.14 %
79.29 %
60.25 %
83.46 %
82.63 %
74.66 %
W. Choi: Near-Online Multi-target Tracking with
Aggregated Local Flow Descriptor
. ICCV 2015.
80
Point3DT
57.20 %
55.71 %
59.15 %
64.66 %
68.67 %
63.20 %
78.30 %
80.07 %
67.56 %
Sukai Wang and M. Liu: PointTrackNet: An End-to-End Network for
3-D Object Detection and Tracking from Point
Clouds . to be submitted ICRA'20 .
81
LP-SSVM*
56.62 %
61.02 %
52.80 %
65.32 %
76.83 %
55.61 %
80.07 %
80.92 %
76.82 %
S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions . International Journal of Computer Vision 2016.
82
MCMOT-CPD
56.61 %
64.28 %
50.55 %
67.37 %
82.77 %
53.96 %
81.97 %
84.26 %
77.98 %
B. Lee, E. Erdenee, S. Jin, M. Nam, Y. Jung and P. Rhee: Multi-class Multi-object Tracking Using Changing Point Detection . ECCVWORK 2016.
83
NOMT
56.49 %
52.29 %
61.59 %
54.73 %
78.75 %
64.63 %
83.40 %
81.41 %
66.36 %
W. Choi: Near-Online Multi-target Tracking with
Aggregated Local Flow Descriptor
. ICCV 2015.
84
SCEA*
56.09 %
60.70 %
52.15 %
64.97 %
77.83 %
54.87 %
81.17 %
81.94 %
74.92 %
J. Yoon, C. Lee, M. Yang and K. Yoon: Online Multi-object Tracking via
Structural Constraint Event Aggregation . IEEE International Conference on
Computer Vision and Pattern Recognition (CVPR) 2016.
85
RMOT*
55.82 %
54.95 %
57.34 %
62.56 %
69.08 %
62.58 %
74.77 %
78.82 %
65.07 %
J. Yoon, M. Yang, J. Lim and K. Yoon: Bayesian Multi-Object Tracking Using
Motion
Context from Multiple Objects . IEEE Winter Conference on
Applications
of
Computer Vision (WACV) 2015.
86
CIWT*
code
54.90 %
60.57 %
49.99 %
64.13 %
78.77 %
51.98 %
82.33 %
81.87 %
74.44 %
A. Osep, W. Mehner, M. Mathias and B. Leibe: Combined Image- and World-Space Tracking
in Traffic Scenes . ICRA 2017.
87
FAMNet
52.56 %
61.00 %
45.51 %
64.40 %
78.67 %
48.66 %
77.41 %
81.47 %
75.92 %
P. Chu and H. Ling: FAMNet: Joint Learning of Feature,
Affinity and Multi-dimensional Assignment for
Online Multiple Object Tracking . ICCV 2019.
88
SASN-MCF_nano
52.24 %
59.65 %
46.22 %
66.28 %
77.27 %
56.20 %
68.77 %
84.56 %
69.82 %
G. Gunduz and T. Acarman: Efficient Multi-Object Tracking by Strong Associations on Temporal Window . IEEE Transactions on Intelligent Vehicles 2019.
89
NOMT-HM
52.17 %
48.58 %
56.45 %
50.76 %
79.02 %
58.78 %
84.62 %
81.82 %
60.68 %
W. Choi: Near-Online Multi-target Tracking with
Aggregated Local Flow Descriptor
. ICCV 2015.
90
SSP*
code
51.16 %
58.96 %
44.64 %
65.26 %
74.09 %
46.75 %
80.78 %
81.32 %
70.94 %
P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow
Tracking with Bounded Memory and Computation . International Conference on Computer
Vision (ICCV) 2015.
91
mbodSSP*
code
50.92 %
58.57 %
44.51 %
63.69 %
75.67 %
46.47 %
81.23 %
81.44 %
70.78 %
P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow
Tracking with Bounded Memory and Computation . International Conference on Computer
Vision (ICCV) 2015.
92
Complexer-YOLO
49.12 %
62.44 %
39.34 %
67.58 %
76.86 %
40.72 %
85.23 %
81.47 %
72.61 %
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object
Detection and Tracking on Semantic Point
Clouds . The IEEE Conference on Computer
Vision and Pattern Recognition (CVPR)
Workshops 2019.
93
LP-SSVM
47.21 %
47.93 %
46.77 %
50.19 %
77.19 %
48.78 %
81.46 %
80.40 %
61.08 %
S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions . International Journal of Computer Vision 2016.
94
DCO-X*
code
46.53 %
56.69 %
38.71 %
62.56 %
74.41 %
41.26 %
79.21 %
81.50 %
66.22 %
A. Milan, K. Schindler and S. Roth: Detection- and Trajectory-Level
Exclusion in Multiple Object Tracking . CVPR 2013.
95
RMOT
44.80 %
42.02 %
48.32 %
44.53 %
73.59 %
51.68 %
77.62 %
78.92 %
51.92 %
J. Yoon, M. Yang, J. Lim and K. Yoon: Bayesian Multi-Object Tracking Using
Motion
Context from Multiple Objects . IEEE Winter Conference on
Applications of
Computer Vision (WACV) 2015.
96
CEM
code
43.41 %
41.72 %
45.77 %
43.72 %
76.72 %
47.45 %
83.68 %
80.44 %
51.34 %
A. Milan, S. Roth and K. Schindler: Continuous Energy Minimization for Multitarget Tracking . IEEE TPAMI 2014.
97
SCEA
43.06 %
44.75 %
41.70 %
46.22 %
80.08 %
43.11 %
84.22 %
81.84 %
56.00 %
J. Yoon, C. Lee, M. Yang and K. Yoon: Online Multi-object Tracking via
Structural Constraint Event Aggregation . IEEE International Conference on
Computer Vision and Pattern Recognition (CVPR) 2016.
98
TBD
code
43.01 %
43.06 %
43.30 %
44.50 %
79.49 %
44.94 %
84.22 %
81.47 %
53.94 %
A. Geiger, M. Lauer, C. Wojek, C. Stiller and R. Urtasun: 3D Traffic Scene Understanding from Movable Platforms . Pattern Analysis and Machine Intelligence (PAMI) 2014. H. Zhang, A. Geiger and R. Urtasun: Understanding High-Level Semantics by Modeling Traffic Patterns . International Conference on Computer Vision (ICCV) 2013.
99
SORT
42.52 %
44.01 %
41.31 %
47.30 %
73.93 %
42.83 %
83.04 %
80.75 %
53.15 %
A. Bewley, Z. Ge, L. Ott, F. Ramos and B. Upcroft: Simple online and realtime tracking . 2016 IEEE International Conference on
Image Processing (ICIP) 2016.
100
SSP
code
40.07 %
44.83 %
36.13 %
46.55 %
78.34 %
39.99 %
75.30 %
80.91 %
56.33 %
P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow
Tracking with Bounded Memory and Computation . International Conference on Computer
Vision (ICCV) 2015.
101
mbodSSP
code
39.49 %
43.94 %
35.82 %
45.72 %
77.85 %
36.95 %
84.35 %
80.76 %
54.10 %
P. Lenz, A. Geiger and R. Urtasun: FollowMe: Efficient Online Min-Cost Flow
Tracking with Bounded Memory and Computation . International Conference on Computer
Vision (ICCV) 2015.
102
ODAMOT
37.05 %
46.53 %
30.07 %
49.91 %
73.20 %
32.46 %
78.19 %
79.26 %
57.03 %
A. Gaidon and E. Vig: Online Domain Adaptation for Multi-Object Tracking . British Machine Vision Conference (BMVC) 2015.
103
FMMOVT
34.35 %
33.80 %
35.39 %
39.20 %
62.79 %
39.66 %
75.42 %
80.40 %
31.23 %
F. Alencar, C. Massera, D. Ridel and D. Wolf: Fast Metric Multi-Object Vehicle Tracking
for Dynamical Environment Comprehension . Latin American Robotics Symposium
(LARS), 2015 2015.
104
MCF
33.98 %
35.97 %
32.32 %
36.87 %
79.67 %
33.65 %
82.48 %
81.31 %
44.40 %
L. Zhang, Y. Li and R. Nevatia: Global data association for multi-object tracking using network flows. . CVPR .
105
HM
33.79 %
34.30 %
33.45 %
35.16 %
79.56 %
34.55 %
83.08 %
81.33 %
42.36 %
A. Geiger: Probabilistic Models for 3D Urban Scene
Understanding from Movable Platforms . 2013.
106
DCO
code
33.45 %
36.33 %
31.30 %
40.93 %
64.11 %
34.23 %
73.46 %
77.25 %
36.72 %
A. Andriyenko, K. Schindler and S. Roth: Discrete-Continuous Optimization
for Multi-Target Tracking . CVPR 2012.
107
DP-MCF
code
25.97 %
35.69 %
19.12 %
36.76 %
78.84 %
28.98 %
39.84 %
81.19 %
36.89 %
H. Pirsiavash, D. Ramanan and C. Fowlkes: Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects . IEEE conference on Computer Vision and Pattern Recognition (CVPR) 2011.