The KITTI Vision Benchmark Suite

The object tracking benchmark consists of 21 training sequences and 29 test sequences. Despite the fact that we have labeled 8 different classes, only the classes 'Car' and 'Pedestrian' are evaluated in our benchmark, as only for those classes enough instances for a comprehensive evaluation have been labeled. The labeling process has been performed in two steps: First we hired a set of annotators, to label 3D bounding boxes as tracklets in point clouds. Since for a pedestrian tracklet, a single 3D bounding box tracklet (dimensions have been fixed) often fits badly, we additionally labeled the left/right boundaries of each object by making use of Mechanical Turk. We also collected labels of the object's occlusion state, and computed the object's truncation via backprojecting a car/pedestrian model into the image plane. We evaluate submitted results using the common metrics CLEAR MOT and MT/PT/ML. Since there is no single ranking criterion, we do not rank methods. Our development kit provides details about the data format as well as utility functions for reading and writing the label files.

• Download left color images of tracking data set (15 GB)
• Download right color images, if you want to use stereo information (15 GB)
• Download Velodyne point clouds, if you want to use laser information (35 GB)
• Download GPS/IMU data, if you want to use map information (8 MB)
• Download camera calibration matrices of tracking data set (1 MB)
• Download training labels of tracking data set (9 MB)
• Download L-SVM reference detections for training and test set (L-SVM), 108 MB)
• Download Regionlet reference detections for training and test set (Regionlets, 33 MB)
• Download tracking development kit (1 MB)

The goal in the object tracking task is to estimate object tracklets for the classes 'Car' and 'Pedestrian'. We evaluate 2D 0-based bounding boxes in each image. We like to encourage people to add a confidence measure for every particular frame for this track. For evaluation we only consider detections/objects larger than 25 pixel (height) in the image and do not count Vans as false positives for cars or Sitting Persons as wrong positives for Pedestrians due to their similarity in appearance. As evaluation criterion we follow the CLEARMOT [1] and Mostly-Tracked/Partly-Tracked/Mostly-Lost [2] metrics. We do not rank methods by a single criterion, but bold numbers indicate the best method for a particular metric. To make the methods comparable, the time for object detection is not included in the specified runtime.

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.

Note 1: On 01.06.2015 we have fixed several bugs in the evaluation script and also in the calculation of the CLEAR MOT metrics. We have furthermore fixed some problems in the annotations of the training and test set (almost completely occluded objects are no longer counted as false negatives). Furthermore, from now on vans are not counted as false positives for cars and sitting persons not as false positives for pedestrians. We have also improved the devkit with new illustrations and re-calculated the results for all methods. Please download the devkit and the annotations/labels with the improved ground truth for training again if you have downloaded the files prior to 20.05.2015. Please consider reporting these new number for all future submissions. The last leaderboards right before the changes can be found here!

Note 2: On 27.11.2015 we have fixed a bug in the evaluation script which prevented van labels from being loaded and led to don't care areas being evaluated. Please download the devkit with the corrected evaluation script (if you want to evaluate on the training set) and consider reporting the new numbers for all future submissions. The leaderboard has been updated. The last leaderboards right before the changes can be found here!

Note 3: On 25.05.2016 we have fixed a bug in the evaluation script wrt. overcounting of ignored detections. Thanks to Adrien Gaidon for reporting this bug. Please download the devkit with the corrected evaluation script (if you want to evaluate on the training set) and consider reporting the new numbers for all future submissions. The leaderboard has been updated. The last leaderboards right before the changes can be found here!

Note 4: On 25.04.2017 a major update of the evaluation script includes the following changes: the counting of ignored detections was corrected; occlusion, truncation and minimum height handling was corrected; and the evaluation summary includes additional statistics. In detail, submitted detections are ignored (i.e. not considered) if they are classified as a "neighboring class" (i.e. 'Van' for 'Car' or 'Cyclist' for 'Pedestrian'), if they do not exceed the minimum height of 25px or if there is an overlap of 0.5 or greater with a 'Don't Care' area. In contrary, ground truth detections are ignored if the occlusion exceeds occlusion level 2, if the truncation exceeds the maximum truncation of 0 or if it belongs to a neighboring class (i.e. 'Van' for 'Car' or 'Cyclist' for 'Pedestrian'). We made sure that true positives, false positives, true negatives and false negatives are counted correctly. Finally, the evaluation summary now includes information about the number of ignored detections. We like to thank the following researchers for detailed feedback: Adrien Gaidon, Jonathan D. Kuck and Jose M. Buenaposada. The last leaderboards right before the changes can be found here!

CAR

	Method	Setting	Code	MOTA	MOTP	MT	ML	IDS	FRAG	Runtime	Environment
1	CasTrack		code	91.93 %	86.19 %	86.77 %	4.00 %	21	107	0.1 s	1 core @ 2.5 Ghz (C/C++)
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.
2	PermaTrack			91.92 %	85.83 %	86.77 %	2.31 %	138	345	0.1 s	GPU @ 2.5 Ghz (Python)
P. Tokmakov, J. Li, W. Burgard and A. Gaidon: Learning to Track with Object Permanence. ICCV 2021.
3	CollabMOT			91.88 %	85.86 %	86.92 %	2.46 %	248	372	0.02 s	4 cores @ 2.5 Ghz (Python)
P. Ninh and H. Kim: CollabMOT Stereo Camera Collaborative Multi Object Tracking. IEEE Access 2024.
4	MCTrack		code	91.79 %	86.92 %	87.08 %	8.00 %	28	53	0.01 s	1 core @ 2.5 Ghz (Python)
X. Wang, S. Qi, J. Zhao, H. Zhou, S. Zhang, G. Wang, K. Tu, S. Guo, J. Zhao, J. Li and M. Yang: MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving. 2024.
5	PC-TCNN			91.75 %	86.17 %	87.54 %	2.92 %	26	118	0.3 s	GPU (python/c++)
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.
6	RAM			91.73 %	85.90 %	87.08 %	2.31 %	255	380	0.09 s	GPU @ 2.5 Ghz (Python)
P. Tokmakov, A. Jabri, J. Li and A. Gaidon: Object Permanence Emerges in a Random Walk along Memory. ICML 2022.
7	BiTrack			91.63 %	87.48 %	85.85 %	7.08 %	12	233	0.01 s	1 core @ 2.5 Ghz (C/C++)
K. Huang, M. Zhang, Y. Chen and Q. Hao: BiTrack: Bidirectional Offline 3D Multi-Object Tracking Using Camera-LiDAR Data. 2024.
8	Rethink MOT			91.47 %	85.63 %	89.38 %	4.31 %	72	180	0.3 s	4 cores @ 2.5 Ghz (Python)
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.
9	RobMOT_v2			91.17 %	86.57 %	83.54 %	10.15 %	19	64	1 s	1 core @ 2.5 Ghz (C/C++)
10	PMTrack			91.16 %	86.87 %	87.38 %	6.62 %	35	89	0.02 s	1 core @ 2.5 Ghz (Python)
11	HybridTrack			91.06 %	86.86 %	85.38 %	8.31 %	32	97	0.01 s	1 core @ 2.5 Ghz (C/C++)
12	McByte			91.05 %	85.71 %	80.15 %	4.00 %	85	151	99 min	GPU @ 2.5 Ghz (Python)
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13	RobMOT			91.04 %	86.56 %	83.54 %	10.15 %	25	71	1 s	1 core @ 2.5 Ghz (C/C++)
M. Nagy, N. Werghi, B. Hassan, J. Dias and M. Khonji: RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud. 2024.
14	DFR			90.98 %	86.55 %	83.85 %	10.00 %	18	66	0.01 s	1 core @ 2.5 Ghz (C/C++)
15	LEGO			90.80 %	86.75 %	87.69 %	1.54 %	173	246	0.01 s	1 core @ 2.5 Ghz (Python)
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.
16	OC-SORT		code	90.64 %	85.71 %	81.23 %	2.92 %	225	471	0.03 s	1 core @ 3.0 Ghz (Python)
J. Cao, X. Weng, R. Khirodkar, J. Pang and K. Kitani: Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. 2022.
17	CasTrack			90.63 %	86.29 %	84.62 %	6.00 %	134	204	1 s	1 core @ 2.5 Ghz (Python)
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 Transactions on Intelligent Transportation Systems 2022.
18	VirConvTrack			90.60 %	86.92 %	84.92 %	8.15 %	115	161	1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi, X. Li and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2023.
19	RobMOT_CasA			90.45 %	86.02 %	83.23 %	10.92 %	23	91	1 s	1 core @ 2.5 Ghz (C/C++)
M. Nagy, N. Werghi, B. Hassan, J. Dias and M. Khonji: RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud. 2024.
20	PNAS-MOT		code	90.42 %	85.62 %	86.77 %	2.31 %	552	762	0.01 s	GPU @ 2.5 Ghz (Python)
C. Peng, Z. Zeng, J. Gao, J. Zhou, M. Tomizuka, X. Wang, C. Zhou and N. Ye: PNAS-MOT: Multi-Modal Object Tracking With Pareto Neural Architecture Search. IEEE Robotics and Automation Letters 2024.
21	RobMOT_CasA			90.37 %	87.01 %	81.69 %	8.31 %	24	372	0.01 s	1 core @ 2.5 Ghz (C/C++)
M. Nagy, N. Werghi, B. Hassan, J. Dias and M. Khonji: RobMOT: Robust 3D Multi-Object Tracking by Observational Noise and State Estimation Drift Mitigation on LiDAR PointCloud. 2024.
22	KFDL			90.34 %	86.67 %	86.15 %	8.31 %	25	106	0.11 s	GPU @ 2.5 Ghz (Python)
23	JHIT			90.29 %	85.61 %	84.77 %	3.23 %	168	251	0.01 s	1 core @ 3.5 Ghz (Python)
P. Claasen and J. Villiers: Interacting Multiple Model-based Joint Homography Matrix and Multiple Object State Estimation. 2024.
24	VirConvTrack		code	90.28 %	86.93 %	83.23 %	11.69 %	12	66	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
25	SRK_ODESA(mc)			90.03 %	84.32 %	82.62 %	2.31 %	90	501	0.4 s	GPU (Python)
D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020.
26	C-TWiX		code	90.03 %	85.62 %	82.15 %	2.92 %	344	620	0.01 s	8 cores @ >3.5 Ghz (Python)
M. Miah, G. Bilodeau and N. Saunier: Learning data association for multi-object tracking using only coordinates. Pattern Recognition 2025.
27	STA-MOT			89.90 %	87.02 %	81.08 %	9.23 %	244	271	0.01 s	1 core @ 2.5 Ghz (Python)
28	MCTrack_online		code	89.86 %	86.94 %	87.69 %	1.23 %	50	373	0.01 s	>8 cores @ 3.5 Ghz (Python)
X. Wang, S. Qi, J. Zhao, H. Zhou, S. Zhang, G. Wang, K. Tu, S. Guo, J. Zhao, J. Li and others: MCTrack: A Unified 3D Multi-Object Tracking Framework for Autonomous Driving. arXiv preprint arXiv:2409.16149 2024.
29	FusionTrack+pointgnn			89.67 %	85.57 %	76.77 %	3.85 %	26	316	0.1 s	1 core @ 2.5 Ghz (C/C++)
30	CollabMOT			89.60 %	85.04 %	82.31 %	2.31 %	123	331	0.05 s	1 core @ 2.5 Ghz (C/C++)
P. Ninh and H. Kim: CollabMOT Stereo Camera Collaborative Multi Object Tracking. IEEE Access 2024.
31	CenterTrack		code	89.44 %	85.05 %	82.31 %	2.31 %	116	334	0.045s	GPU
X. Zhou, V. Koltun and P. Krähenbühl: Tracking Objects as Points. ECCV 2020.
32	APPTracker+			89.44 %	85.15 %	78.62 %	3.85 %	125	415	0.04 s	GPU @ 1.5 Ghz (Python)
T. Zhou, Q. Ye, W. Luo, H. Ran, Z. Shi and J. Chen: APPTracker+: Displacement Uncertainty for Occlusion Handling in Low-Frame-Rate Multiple Object Tracking. International Journal of Computer Vision 2024.
33	S3Track			88.97 %	87.25 %	86.92 %	1.69 %	154	369	0.03 s	1 core @ 2.5 Ghz (Python)
Anonymous: S$^3$Track: Self-supervised Tracking with Soft Assignment Flow. .
34	DEFT		code	88.95 %	84.55 %	84.77 %	1.85 %	343	553	0.04 s	GPU @ 2.5 Ghz (Python)
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.
35	PC3T		code	88.88 %	84.37 %	80.00 %	8.31 %	208	369	0.0045 s	1 core @ >3.5 Ghz (Python + C/C++)
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.
36	Mono_3D_KF			88.77 %	83.95 %	80.46 %	3.69 %	96	218	0.3 s	1 core @ 2.5 Ghz (Python)
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.
37	SRK_ODESA(hc)			88.65 %	85.70 %	78.92 %	2.15 %	133	582	0.4 s	GPU @ 2.5 Ghz (Python)
D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020.
38	EagerMOT		code	88.21 %	85.73 %	76.62 %	2.46 %	121	474	0.011 s	4 cores @ 3.0 Ghz (Python)
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.
39	MSA-MOT			88.19 %	85.47 %	87.23 %	1.23 %	56	405	0.01 s	1 core @ 2.5 Ghz (Python)
Z. Zhu, J. Nie, H. Wu, Z. He and M. Gao: MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking. Sensors 2022.
40	YONTD-MOTv2		code	88.17 %	86.27 %	80.31 %	2.62 %	30	327	0.1 s	GPU @ >3.5 Ghz (Python)
X. Wang, C. Fu, J. He, M. Huang, T. Meng, S. Zhang, H. Zhou, Z. Xu and C. Zhang: A Multi-Modal Fusion-Based 3D Multi- Object Tracking Framework with Joint Detection. IEEE Robotics and Automation Letters 2024.
41	UG3DMOT		code	88.10 %	86.58 %	79.23 %	5.38 %	5	330	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. He, C. Fu, X. Wang and J. Wang: 3D multi-object tracking based on informatic divergence-guided data association. Signal Processing 2024.
42	LGM			88.06 %	84.16 %	85.54 %	2.15 %	469	590	0.08 s	GPU @ 2.5 Ghz (Python)
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.
43	TrackMPNN		code	87.74 %	84.55 %	84.77 %	1.85 %	404	607	0.05 s	4 cores @ 3.0 Ghz (Python)
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 .
44	Stereo3DMOT		code	87.13 %	85.17 %	75.85 %	9.38 %	19	533	0.06 s	1 core @ 2.5 Ghz (C/C++)
C. Mao, C. Tan, H. Liu, J. Hu and M. Zheng: Stereo3DMOT: Stereo Vision Based 3D Multi-object Tracking with Multimodal ReID. Chinese Conference on Pattern Recognition and Computer Vision (PRCV) 2023.
45	S3MOT		code	86.96 %	86.56 %	84.77 %	1.23 %	582	762	0.03 s	1 core @ 2.5 Ghz (Python)
46	SpbTracker			86.95 %	86.21 %	74.92 %	4.77 %	116	544	0.07 s	2 cores @ 2.5 Ghz (Python + C/C++)
E. Im, C. Jee and J. Lee: Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environmen. arXiv preprint arXiv:2408.05940 2024.
47	TuSimple			86.62 %	83.97 %	72.46 %	6.77 %	293	501	0.6 s	1 core @ 2.5 Ghz (Matlab + C/C++)
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.
48	BcMODT			86.53 %	85.37 %	78.31 %	2.62 %	45	626	0.01 s	GPU @ 2.5 Ghz (Python)
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.
49	QD-3DT		code	86.41 %	85.82 %	75.38 %	2.46 %	108	553	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
50	JMODT		code	86.27 %	85.41 %	77.38 %	2.92 %	45	585	0.01 s	GPU @ 2.5 Ghz (Python)
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.
51	Quasi-Dense		code	85.76 %	85.01 %	69.08 %	3.08 %	93	617	0.07s	GPU (Python)
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.
52	JRMOT		code	85.70 %	85.48 %	71.85 %	4.00 %	98	372	0.07 s	4 cores @ 2.5 Ghz (Python)
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.
53	StrongFusion-MOT			85.63 %	85.17 %	66.15 %	6.00 %	34	399	0.01 s	8 cores @ 2.5 Ghz (Python)
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.
54	RA3DMOT			85.56 %	87.19 %	83.38 %	1.85 %	57	622	0.01 s	GPU @ 2.5 Ghz (Python)
55	PolarMOT		code	85.31 %	85.52 %	81.38 %	2.31 %	408	900	0.02 s	1 core @ 2.5 Ghz (C/C++)
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.
56	YONTD-MOT		code	85.19 %	87.10 %	67.54 %	7.08 %	21	342	0.1 s	GPU @ >3.5 Ghz (Python)
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.
57	3DMLA			85.12 %	84.91 %	70.62 %	5.85 %	15	318	0.02 s	1 core @ 2.5 Ghz (C/C++)
M. Cho and E. Kim: 3D LiDAR Multi-Object Tracking with Short-Term and Long-Term Multi-Level Associations. Remote Sensing 2023.
58	EAFFMOT			85.04 %	85.13 %	70.92 %	8.31 %	15	256	0.01 s	1 core @ 2.5 Ghz (C/C++)
J. Jin, J. Zhang, K. Zhang, Y. Wang, Y. Ma and D. Pan: 3D multi-object tracking with boosting data association and improved trajectory management mechanism. Signal Processing 2024.
59	MASS			85.04 %	85.53 %	74.31 %	2.77 %	301	744	0.01s	C++
H. Karunasekera, H. Wang and H. Zhang: Multiple Object Tracking with attention to Appearance, Structure, Motion and Size. IEEE Access 2019.
60	MOTSFusion		code	84.83 %	85.21 %	73.08 %	2.77 %	275	759	0.44s	GPU (Python)
J. Luiten, T. Fischer and B. Leibe: Track to Reconstruct and Reconstruct to Track. IEEE Robotics and Automation Letters 2020.
61	DeepFusion-MOT		code	84.80 %	85.10 %	68.46 %	9.08 %	35	444	0.01 s	>8 cores @ 2.5 Ghz (Python)
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.
62	mmMOT		code	84.77 %	85.21 %	73.23 %	2.77 %	284	753	0.02s	GPU @ 2.5 Ghz (Python)
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.
63	TripletTrack			84.77 %	86.16 %	69.54 %	3.38 %	222	646	0.1 s	1 core @ 2.5 Ghz (C/C++)
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.
64	FNC2			84.75 %	85.80 %	76.00 %	5.85 %	33	311	0.01 s	1 core @ 3.0 Ghz (C/C++)
C. Jiang, Z. Wang, H. Liang and Y. Wang: A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking. IEEE Transactions on Intelligent Vehicles 2024. C. Jiang, Z. Wang and H. Liang: A Fast and High-Performance Object Proposal Method for Vision Sensors: Application to Object Detection. IEEE Sensors Journal 2022.
65	DiTMOT		code	84.73 %	84.40 %	74.92 %	12.92 %	31	188	0.08 s	1 core @ >3.5 Ghz (Python)
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.
66	mono3DT		code	84.52 %	85.64 %	73.38 %	2.77 %	377	847	0.03 s	GPU @ 2.5 Ghz (Python)
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.
67	SMAT			84.27 %	86.09 %	63.08 %	5.38 %	28	341	0.1 s	1 core @ 2.5 Ghz (C/C++)
N. Gonzalez, A. Ospina and P. Calvez: SMAT: Smart Multiple Affinity Metrics for Multiple Object Tracking. Image Analysis and Recognition 2020.
68	MOTBeyondPixels		code	84.24 %	85.73 %	73.23 %	2.77 %	468	944	0.3 s	1 core @ 2.5 Ghz (C/C++)
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.
69	AB3DMOT+PointRCNN		code	83.92 %	85.30 %	66.77 %	9.08 %	10	199	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng, J. Wang, D. Held and K. Kitani: 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics. IROS 2020.
70	MO-YOLO		code	83.55 %	84.61 %	72.00 %	5.23 %	252	569	0.024 s	2080ti (Python)
L. Pan, Y. Feng, W. Di, L. Bo and Z. Xingle: MO-YOLO: End-to-End Multiple-Object Tracking Method with YOLO and MOTR. arXiv preprint arXiv:2310.17170 2023.
71	IMMDP			83.04 %	82.74 %	60.62 %	11.38 %	172	365	0.19 s	4 cores @ >3.5 Ghz (Matlab + C/C++)
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.
72	aUToTrack			82.25 %	80.52 %	72.62 %	3.54 %	1025	1402	0.01 s	1 core @ >3.5 Ghz (C/C++)
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.
73	\		code	80.83 %	78.73 %	73.85 %	3.23 %	16	330	0.01 s	1 core @ 2.5 Ghz (C/C++)
74	JCSTD			80.57 %	81.81 %	56.77 %	7.38 %	61	643	0.07 s	1 core @ 2.7 Ghz (C++)
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.
75	3D-CNN/PMBM			80.39 %	81.26 %	62.77 %	6.15 %	121	613	0.01 s	1 core @ 3.0 Ghz (Matlab)
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.
76	extraCK			79.99 %	82.46 %	62.15 %	5.54 %	343	938	0.03 s	1 core @ 2.5 Ghz (Python)
G. Gunduz and T. Acarman: A lightweight online multiple object vehicle tracking method. Intelligent Vehicles Symposium (IV), 2018 IEEE 2018.
77	NC2			78.95 %	85.82 %	76.00 %	5.69 %	31	275	0.01 s	1 core @ 3.0 Ghz (C/C++)
C. Jiang, Z. Wang, H. Liang and Y. Wang: A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking. IEEE Transactions on Intelligent Vehicles 2024.
78	MCMOT-CPD			78.90 %	82.13 %	52.31 %	11.69 %	228	536	0.01 s	1 core @ 3.5 Ghz (Python)
B. Lee, E. Erdenee, S. Jin, M. Nam, Y. Jung and P. Rhee: Multi-class Multi-object Tracking Using Changing Point Detection. ECCVWORK 2016.
79	NOMT*			78.15 %	79.46 %	57.23 %	13.23 %	31	207	0.09 s	16 cores @ 2.5 Ghz (C++)
W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
80	FANTrack		code	77.72 %	82.33 %	62.62 %	8.77 %	150	812	0.04 s	8 cores @ >3.5 Ghz (Python)
E. Baser, V. Balasubramanian, P. Bhattacharyya and K. Czarnecki: FANTrack: 3D Multi-Object Tracking with Feature Association Network. ArXiv 2019.
81	LP-SSVM*			77.63 %	77.80 %	56.31 %	8.46 %	62	539	0.02 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016.
82	FAMNet			77.08 %	78.79 %	51.38 %	8.92 %	123	713	1.5 s	GPU @ 1.0 Ghz (Python)
P. Chu and H. Ling: FAMNet: Joint Learning of Feature, Affinity and Multi-dimensional Assignment for Online Multiple Object Tracking. ICCV 2019.
83	MDP		code	76.59 %	82.10 %	52.15 %	13.38 %	130	387	0.9 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
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.
84	DSM			76.15 %	83.42 %	60.00 %	8.31 %	296	868	0.1 s	GPU @ 1.0 Ghz (Python)
D. Frossard and R. Urtasun: End-To-End Learning of Multi-Sensor 3D Tracking by Detection. ICRA 2018.
85	Complexer-YOLO			75.70 %	78.46 %	58.00 %	5.08 %	1186	2092	0.01 a	GPU @ 3.5 Ghz (C/C++)
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.
86	SCEA*			75.58 %	79.39 %	53.08 %	11.54 %	104	448	0.06 s	1 core @ 4.0 Ghz (Matlab + C/C++)
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.
87	CIWT*		code	75.39 %	79.25 %	49.85 %	10.31 %	165	660	0.28 s	1 core @ 2.5 Ghz (C/C++)
A. Osep, W. Mehner, M. Mathias and B. Leibe: Combined Image- and World-Space Tracking in Traffic Scenes. ICRA 2017.
88	NOMT-HM*			75.20 %	80.02 %	50.00 %	13.54 %	105	351	0.09 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
89	SSP*		code	72.72 %	78.55 %	53.85 %	8.00 %	185	932	0.6 s	1 core @ 2.7 Ghz (Python)
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.
90	mbodSSP*		code	72.69 %	78.75 %	48.77 %	8.77 %	114	858	0.01 s	1 core @ 2.7 Ghz (Python)
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	SASN-MCF_nano			70.86 %	82.65 %	58.00 %	7.85 %	443	975	0.02 s	1 core @ 3.0 Ghz (Python)
G. Gunduz and T. Acarman: Efficient Multi-Object Tracking by Strong Associations on Temporal Window. IEEE Transactions on Intelligent Vehicles 2019.
92	Point3DT			68.24 %	76.57 %	60.62 %	12.31 %	111	725	0.05 s	1 core @ >3.5 Ghz (Python)
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 .
93	DCO-X*		code	68.11 %	78.85 %	37.54 %	14.15 %	318	959	0.9 s	1 core @ >3.5 Ghz (Matlab + C/C++)
A. Milan, K. Schindler and S. Roth: Detection- and Trajectory-Level Exclusion in Multiple Object Tracking. CVPR 2013.
94	SST [st]			67.38 %	83.98 %	43.08 %	20.15 %	13	212	1 s	1 core @ 2.5 Ghz (C/C++)
95	NOMT			66.60 %	78.17 %	41.08 %	25.23 %	13	150	0.09 s	16 core @ 2.5 Ghz (C++)
W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
96	RMOT*			65.83 %	75.42 %	40.15 %	9.69 %	209	727	0.02 s	1 core @ 3.5 Ghz (Matlab)
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.
97	MLA-MOT			64.98 %	81.69 %	28.92 %	28.31 %	28	339	0.1 s	GPU @ 2.5 Ghz (Python)
98	LP-SSVM			61.77 %	76.93 %	35.54 %	21.69 %	16	422	0.05 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016.
99	NOMT-HM			61.17 %	78.65 %	33.85 %	28.00 %	28	241	0.09 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
100	ODAMOT			59.23 %	75.45 %	27.08 %	15.54 %	389	1274	1 s	1 core @ 2.5 Ghz (Python)
A. Gaidon and E. Vig: Online Domain Adaptation for Multi-Object Tracking. British Machine Vision Conference (BMVC) 2015.
101	SSP		code	57.85 %	77.64 %	29.38 %	24.31 %	7	704	0.6s	1 core @ 2.7 Ghz (Python)
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	SCEA			57.03 %	78.84 %	26.92 %	26.62 %	17	461	0.05 s	1 core @ 4.0 Ghz (Matlab + C/C++)
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.
103	mbodSSP		code	56.03 %	77.52 %	23.23 %	27.23 %	0	699	0.01 s	1 core @ 2.7 Ghz (Python)
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.
104	TBD		code	55.07 %	78.35 %	20.46 %	32.62 %	31	529	10 s	1 core @ 2.5 Ghz (Matlab + C/C++)
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.
105	SORT			54.22 %	77.57 %	25.69 %	29.08 %	1	557	.002 s	1 core @ 2.5 Ghz (Python)
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.
106	RMOT			52.42 %	75.18 %	21.69 %	31.85 %	50	376	0.01 s	1 core @ 3.5 Ghz (Matlab)
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.
107	CEM		code	51.94 %	77.11 %	20.00 %	31.54 %	125	396	0.09 s	1 core @ >3.5 Ghz (Matlab + C/C++)
A. Milan, S. Roth and K. Schindler: Continuous Energy Minimization for Multitarget Tracking. IEEE TPAMI 2014.
108	MCF			45.92 %	78.25 %	14.92 %	37.23 %	21	581	0.01 s	1 core @ 2.5 Ghz (Python + C/C++)
L. Zhang, Y. Li and R. Nevatia: Global data association for multi-object tracking using network flows.. CVPR .
109	HM			43.85 %	78.34 %	12.46 %	39.54 %	12	571	0.01 s	1 core @ 2.5 Ghz (Python)
A. Geiger: Probabilistic Models for 3D Urban Scene Understanding from Movable Platforms. 2013.
110	DP-MCF		code	38.33 %	78.41 %	18.00 %	36.15 %	2716	3225	0.01 s	1 core @ 2.5 Ghz (Matlab)
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.
111	DCO		code	37.28 %	74.36 %	15.54 %	30.92 %	220	612	0.03 s	1 core @ >3.5 Ghz (Matlab + C/C++)
A. Andriyenko, K. Schindler and S. Roth: Discrete-Continuous Optimization for Multi-Target Tracking. CVPR 2012.
112	FMMOVT			31.88 %	77.68 %	21.38 %	34.92 %	511	930	0.05 s	1 core @ 2.5 Ghz (C/C++)
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.
113	tflf			-101.19 %	59.48 %	0.00 %	100.00 %	0	0	35 s	1 core @ 2.5 Ghz (Python)

PEDESTRIAN

	Method	Setting	Code	MOTA	MOTP	MT	ML	IDS	FRAG	Runtime	Environment
1	SRK_ODESA(mp)			69.88 %	75.07 %	45.02 %	8.25 %	191	1070	0.5 s	GPU (Python)
D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020.
2	SRK_ODESA(hp)			69.24 %	75.07 %	45.02 %	8.25 %	340	1181	0.5 s	GPU @ 2.0 Ghz (Python)
D. Mykheievskyi, D. Borysenko and V. Porokhonskyy: Learning Local Feature Descriptors for Multiple Object Tracking. ACCV 2020.
3	RAM			67.33 %	73.83 %	52.23 %	13.40 %	403	1077	0.09 s	GPU @ 2.5 Ghz (Python)
P. Tokmakov, A. Jabri, J. Li and A. Gaidon: Object Permanence Emerges in a Random Walk along Memory. ICML 2022.
4	PermaTrack			65.76 %	74.67 %	49.14 %	15.12 %	124	792	0.1 s	GPU @ 2.5 Ghz (Python)
P. Tokmakov, J. Li, W. Burgard and A. Gaidon: Learning to Track with Object Permanence. ICCV 2021.
5	McByte			65.52 %	74.69 %	40.55 %	21.99 %	170	674	99 min	GPU @ 2.5 Ghz (Python)
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6	C-TWiX		code	64.32 %	75.52 %	42.61 %	17.53 %	236	896	0.01 s	8 cores @ >3.5 Ghz (Python)
M. Miah, G. Bilodeau and N. Saunier: Learning data association for multi-object tracking using only coordinates. Pattern Recognition 2025.
7	OC-SORT		code	64.01 %	74.73 %	44.67 %	19.59 %	161	813	0.03 s	1 core @ 3.0 Ghz (Python)
J. Cao, X. Weng, R. Khirodkar, J. Pang and K. Kitani: Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking. 2022.
8	JHIT			63.13 %	74.60 %	45.02 %	19.24 %	463	964	0.01 s	1 core @ 3.5 Ghz (Python)
P. Claasen and J. Villiers: Interacting Multiple Model-based Joint Homography Matrix and Multiple Object State Estimation. 2024.
9	AHMOT		code	60.12 %	71.09 %	52.92 %	9.97 %	466	1296	0.01 s	1 core @ 2.5 Ghz (C/C++)
10	\		code	58.48 %	71.14 %	53.26 %	9.97 %	460	1323	0.01 s	1 core @ 2.5 Ghz (C/C++)
11	TuSimple			58.15 %	71.93 %	30.58 %	24.05 %	138	818	0.6 s	1 core @ 2.5 Ghz (Matlab + C/C++)
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.
12	Quasi-Dense		code	56.81 %	73.99 %	31.27 %	18.90 %	254	1121	0.07s	GPU (Python)
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.
13	MMTrack			56.69 %	75.51 %	31.62 %	32.65 %	76	522	0.0135s	GPU
L. Xu and Y. Huang: Rethinking Joint Detection and Embedding for Multiobject Tracking in Multiscenario. IEEE Transactions on Industrial Informatics 2024.
14	FNC2			56.52 %	66.07 %	43.99 %	12.37 %	349	1492	0.01 s	1 core @ 3.0 Ghz (C/C++)
C. Jiang, Z. Wang, H. Liang and Y. Wang: A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking. IEEE Transactions on Intelligent Vehicles 2024. C. Jiang, Z. Wang and H. Liang: A Fast and High-Performance Object Proposal Method for Vision Sensors: Application to Object Detection. IEEE Sensors Journal 2022.
15	APPTracker+			56.20 %	74.54 %	32.30 %	25.43 %	90	854	0.04 s	GPU @ 1.5 Ghz (Python)
T. Zhou, Q. Ye, W. Luo, H. Ran, Z. Shi and J. Chen: APPTracker+: Displacement Uncertainty for Occlusion Handling in Low-Frame-Rate Multiple Object Tracking. International Journal of Computer Vision 2024.
16	MO-YOLO		code	55.71 %	73.93 %	34.02 %	35.40 %	121	797	0.024 s	2080ti (Python)
L. Pan, Y. Feng, W. Di, L. Bo and Z. Xingle: MO-YOLO: End-to-End Multiple-Object Tracking Method with YOLO and MOTR. arXiv preprint arXiv:2310.17170 2023.
17	CenterTrack		code	55.34 %	74.02 %	34.71 %	19.93 %	95	751	0.045s	GPU
X. Zhou, V. Koltun and P. Krähenbühl: Tracking Objects as Points. ECCV 2020.
18	3D-TLSR			54.00 %	73.03 %	29.55 %	23.71 %	100	835		1 core @ 2.5 Ghz (C/C++)
U. Nguyen and C. Heipke: 3D Pedestrian tracking using local structure constraints. ISPRS Journal of Photogrammetry and Remote Sensing 2020.
19	SpbTracker			53.45 %	65.54 %	31.62 %	28.18 %	250	1300	0.07 s	2 cores @ 2.5 Ghz (Python + C/C++)
E. Im, C. Jee and J. Lee: Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environmen. arXiv preprint arXiv:2408.05940 2024.
20	TrackMPNN		code	53.22 %	73.69 %	33.68 %	18.56 %	395	1035	0.05 s	4 cores @ 3.0 Ghz (Python)
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 .
21	QD-3DT		code	52.98 %	73.41 %	32.30 %	18.56 %	488	1393	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
22	CAT			52.35 %	71.57 %	34.36 %	23.71 %	206	804
U. Nguyen, F. Rottensteiner and C. Heipke: CONFIDENCE-AWARE PEDESTRIAN TRACKING USING A STEREO CAMERA. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 2019.
23	Be-Track			51.29 %	72.71 %	20.96 %	31.27 %	118	848	0.02 s	GPU @ 1.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Behavioral Pedestrian Tracking Using a Camera and LiDAR Sensors on a Moving Vehicle. Sensors 2019.
24	EagerMOT		code	51.11 %	64.75 %	27.84 %	24.05 %	234	1378	0.011 s	4 cores @ 3.0 Ghz (Python)
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.
25	TripletTrack			50.85 %	74.17 %	22.68 %	28.87 %	139	986	0.1 s	1 core @ 2.5 Ghz (C/C++)
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.
26	MSA-MOT			47.84 %	64.64 %	33.33 %	16.15 %	244	1393	0.01 s	1 core @ 2.5 Ghz (Python)
Z. Zhu, J. Nie, H. Wu, Z. He and M. Gao: MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking. Sensors 2022.
27	PolarMOT		code	47.25 %	64.87 %	30.24 %	18.56 %	241	1375	0.02 s	1 core @ 2.5 Ghz (C/C++)
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.
28	MDP		code	47.22 %	70.36 %	24.05 %	27.84 %	87	825	0.9 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
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.
29	MPNTrack		code	46.92 %	71.84 %	42.96 %	10.65 %	196	1151	0.02 s	8 cores @ 2.5 Ghz (Python)
G. Brasó and L. Leal-Taixé: Learning a Neural Solver for Multiple Object Tracking. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020. G. Bras\'o, O. Cetintas and L. Leal-Taix\'e: Multi-Object Tracking and Segmentation Via Neural Message Passing. International Journal of Computer Vision 2022.
30	NOMT*			46.62 %	71.45 %	26.12 %	34.02 %	63	666	0.09 s	16 cores @ 2.5 Ghz (C++)
W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
31	JRMOT		code	46.33 %	72.54 %	23.37 %	28.87 %	345	1111	0.07 s	4 cores @ 2.5 Ghz (Python)
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.
32	MCMOT-CPD			45.94 %	72.44 %	20.62 %	34.36 %	143	764	0.01 s	1 core @ 3.5 Ghz (Python)
B. Lee, E. Erdenee, S. Jin, M. Nam, Y. Jung and P. Rhee: Multi-class Multi-object Tracking Using Changing Point Detection. ECCVWORK 2016.
33	Mono_3D_KF			45.02 %	69.45 %	32.99 %	25.43 %	203	850	0.3 s	1 core @ 2.5 Ghz (Python)
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.
34	NC2			44.64 %	66.08 %	43.99 %	13.06 %	348	1488	0.01 s	1 core @ 3.0 Ghz (C/C++)
C. Jiang, Z. Wang, H. Liang and Y. Wang: A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking. IEEE Transactions on Intelligent Vehicles 2024.
35	JCSTD			44.20 %	72.09 %	16.49 %	33.68 %	53	917	0.07 s	1 core @ 2.7 Ghz (C++)
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.
36	SCEA*			43.91 %	71.86 %	16.15 %	43.30 %	56	641	0.06 s	1 core @ 4.0 Ghz (Matlab + C/C++)
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.
37	RMOT*			43.77 %	71.02 %	19.59 %	41.24 %	153	748	0.02 s	1 core @ 3.5 Ghz (Matlab)
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.
38	LP-SSVM*			43.76 %	70.48 %	20.62 %	34.36 %	73	809	0.02 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016.
39	CIWT*		code	43.37 %	71.44 %	13.75 %	34.71 %	112	901	0.28 s	1 core @ 2.5 Ghz (C/C++)
A. Osep, W. Mehner, M. Mathias and B. Leibe: Combined Image- and World-Space Tracking in Traffic Scenes. ICRA 2017.
40	EAFFMOT			42.32 %	64.89 %	21.99 %	35.40 %	233	1141	0.01 s	1 core @ 2.5 Ghz (C/C++)
J. Jin, J. Zhang, K. Zhang, Y. Wang, Y. Ma and D. Pan: 3D multi-object tracking with boosting data association and improved trajectory management mechanism. Signal Processing 2024.
41	HybridTrack			39.57 %	64.66 %	21.99 %	48.80 %	192	882	0.01 s	1 core @ 2.5 Ghz (C/C++)
42	NOMT-HM*			39.26 %	71.14 %	21.31 %	41.92 %	184	863	0.09 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
43	StrongFusion-MOT			39.14 %	64.22 %	26.12 %	21.99 %	241	1467	0.01 s	>8 cores @ 2.5 Ghz (Python + C/C++)
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.
44	AB3DMOT+PointRCNN		code	38.39 %	64.88 %	23.02 %	43.99 %	218	940	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng, J. Wang, D. Held and K. Kitani: 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics. IROS 2020.
45	NOMT			36.93 %	67.75 %	17.87 %	42.61 %	34	789	0.09 s	16 core @ 2.5 Ghz (C++)
W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
46	RMOT			34.54 %	68.06 %	14.43 %	47.42 %	81	685	0.01 s	1 core @ 3.5 Ghz (Matlab)
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.
47	LP-SSVM			33.33 %	67.38 %	12.37 %	45.02 %	72	818	0.05 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Wang and C. Fowlkes: Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions. International Journal of Computer Vision 2016.
48	SCEA			33.13 %	68.45 %	9.62 %	46.74 %	16	717	0.05 s	1 core @ 4.0 Ghz (Matlab + C/C++)
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.
49	MLA-MOT			30.39 %	77.20 %	11.68 %	60.48 %	35	354	0.1 s	GPU @ 2.5 Ghz (Python)
50	YONTD-MOT		code	28.93 %	65.99 %	11.00 %	31.96 %	404	1697	0.1 s	GPU @ >3.5 Ghz (Python)
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.
51	CEM		code	27.54 %	68.48 %	8.93 %	51.89 %	96	608	0.09 s	1 core @ >3.5 Ghz (Matlab + C/C++)
A. Milan, S. Roth and K. Schindler: Continuous Energy Minimization for Multitarget Tracking. IEEE TPAMI 2014.
52	NOMT-HM			27.49 %	67.99 %	15.12 %	50.52 %	73	732	0.09 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
W. Choi: Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor . ICCV 2015.
53	SST [st]			17.71 %	65.22 %	9.97 %	67.01 %	110	674	1 s	1 core @ 2.5 Ghz (C/C++)
54	Complexer-YOLO			16.46 %	62.69 %	2.41 %	38.14 %	527	1636	0.01 a	GPU @ 3.5 Ghz (C/C++)
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.
55	tflf			-60.06 %	0.00 %	0.00 %	0.00 %	0	0	35 s	1 core @ 2.5 Ghz (Python)

• TUD Datasets: "TUD Multiview Pedestrians" and "TUD Stadmitte" Datasets.
• PETS 2009: The Datasets for the "Performance Evaluation of Tracking and Surveillance"" Workshop.
• EPFL Terrace: Multi-camera pedestrian videos.
• ETHZ Sequences: Inner City Sequences from Mobile Platforms.

When using this dataset in your research, we will be happy if you cite us:
@inproceedings{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}