MOTS Results

Download all results for this benchmark

Click on a measure to sort the table accordingly. See below for a more detailed description.


Benchmark Statistics

TrackersMOTSAIDF1MOTSAMOTSPMODSAMTMLTPFPFNRcllPrcnID Sw.FragHz
ReMOTSv2
1. using public detections 		70.4 75.8 84.4 84.0 85.1 248 (75.6)12 (3.7)28,270 819 3,999 87.6 97.2 229 (261.4)579 (660.9)0.3
F. Yan, Z. Wang, Y. Wu, S. Sakti, S. Nakamura. Tackling multiple object tracking with complicated motions — Re-designing the integration of motion and appearance. In Image and Vision Computing, 2022.
ReMOTS_
2. using undisclosed detections 		70.4 75.0 84.4 84.0 85.1 248 (75.6)12 (3.7)28,270 819 3,999 87.6 97.2 231 (263.7)579 (660.9)0.3
F. Yang, X. Chang, C. Dang, Z. Zheng, S. Sakti, S. Nakamura, Y. Wu. ReMOTS: Refining Multi-Object Tracking and Segmentation. In , 2020.
EMNT
3. online method 		70.0 77.0 83.7 84.1 84.5 234 (71.3)13 (4.0)27,943 666 4,326 86.6 97.7 261 (301.4)618 (713.7)11.7
S. Wang, H. Sheng, D. Yang, Y. Zhang, Y. Wu and S. Wang, "Extendable Multiple Nodes Recurrent Tracking Framework with RTU++," in IEEE Transactions on Image Processing, 2022, doi: 10.1109/TIP.2022.3192706.
MAF_HDA
4. online method 		69.9 67.0 83.8 84.1 85.1 245 (74.7)12 (3.7)28,311 863 3,958 87.7 97.0 401 (457.1)598 (681.6)4.6
Y. Song, Y. Yoon, K. Yoon, M. Jeon. Multi-Object Tracking and Segmentation with Embedding Mask-based Affinity Fusion in Hierarchical Data Association. In IEEE Access, 2022.
GMPHD_MAF
5. online method 		69.4 66.4 83.3 84.2 84.8 249 (75.9)11 (3.4)28,284 935 3,985 87.7 96.8 484 (552.2)711 (811.2)2.6
Y. Song, Y. Yoon, K. Yoon, M. Jeon, S. Lee, W. Pedrycz. Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion. In [Online]. Available: arXiv:2009.00100, 2021.
OPITrack_
6. online method 		63.5 45.4 75.5 84.6 76.6 218 (66.5)28 (8.5)25,152 441 7,117 77.9 98.3 342 (438.8)583 (748.0)3,382.2
Y. Gao, H. Xu, Y. Zheng, J. Li and X. Gao, "An Object Point Set Inductive Tracker for Multi-Object Tracking and Segmentation," in IEEE Transactions on Image Processing, 2022, doi: 10.1109/TIP.2022.3203607.
ZXPointTrack
7. online method using undisclosed detections 		62.3 42.9 76.8 82.3 78.4 186 (56.7)41 (12.5)26,276 963 5,993 81.4 96.5 541 (664.4)868 (1,066.0)4.6
TBD
CCP
8. online method 		59.3 58.1 75.5 80.3 77.0 204 (62.2)32 (9.8)26,610 1,759 5,659 82.5 93.8 484 (586.9)645 (782.2)10.5
TBD
MPNTrackSeg
9. 		58.6 68.8 73.7 80.6 74.3 207 (63.1)26 (7.9)25,036 1,059 7,233 77.6 95.9 202 (260.4)635 (818.5)2.3
G. Bras'o, O. Cetintas, L. Leal-Taix'e. Multi-Object Tracking and Segmentation Via Neural Message Passing. In International Journal of Computer Vision, 2022.
SORTS_ReID
10. online method using public detections 		55.8 65.8 69.1 81.9 70.0 107 (32.6)52 (15.9)23,671 1,076 8,598 73.4 95.7 304 (414.4)576 (785.2)36.4
Not yet published
TrackersMOTSAIDF1MOTSAMOTSPMODSAMTMLTPFPFNRcllPrcnID Sw.FragHz
SORTS
11. online method using undisclosed detections 		55.0 57.3 68.3 81.9 70.0 107 (32.6)52 (15.9)23,671 1,076 8,598 73.4 95.7 552 (752.5)577 (786.6)54.5
M. Ahrnbom, M. Nilsson, H. Ard"o. Real-time and Online Segmentation Multi-target Tracking with Track Revival Re-identification.. In VISIGRAPP (5: VISAPP), 2021.
TrackRCNN
12. using public detections 		40.6 42.4 55.2 76.1 56.9 127 (38.7)71 (21.6)19,628 1,261 12,641 60.8 94.0 567 (932.2)868 (1,427.0)2.0
P. Voigtlaender, M. Krause, A. O\usep, J. Luiten, B. Sekar, A. Geiger, B. Leibe. MOTS: Multi-Object Tracking and Segmentation. In CVPR, 2019.
SequencesFramesTrajectoriesBoxes
4304432832269

Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
sMOTSA higher 100%Mask-based Soft Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. Soft version of MOTSA, accumulates the mask overlaps of true positives instead of only counting how many masks reach an IoU of more than 0.5.
IDF1 higher 100%ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections.
MOTSA higher 100%Mask-based Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. Variant of MOTA, evaluated based on mask overlap (mask IoU)
MOTSP higher 100%Mask-overlap based variant of Multi-Object Tracking Precision. [1]. Variant of MOTP, evaluated based on mask IoU instead of bounding box IoU.
MODSA higher 100%Mask-overlap based Multi-Object Detection Accuracy [1]. Variant of MODA, evaluated based on the mask overlap (mask IoU).
MT higher 100%Mostly tracked targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at least 80% of their respective life span.
ML lower 0%Mostly lost targets. The ratio of ground-truth trajectories that are covered by a track hypothesis for at most 20% of their respective life span.
TP higher #GTThe total number of true positives.
FP lower 0The total number of false positives.
FN lower 0The total number of false negatives (missed targets).
Rcll higher 100%Ratio of correct detections to total number of GT boxes.
Prcn higher 100%Ratio of TP / (TP+FP).
ID Sw. lower 0Number of Identity Switches (ID switch ratio = #ID switches / recall) [3]. Please note that we follow the stricter definition of identity switches as described in the reference
Frag lower 0The total number of times a trajectory is fragmented (i.e. interrupted during tracking).
Hz higher Inf.Processing speed (in frames per second excluding the detector) on the benchmark. The frequency is provided by the authors and not officially evaluated by the MOTChallenge.

Legend

Symbol Description
online method This is an online (causal) method, i.e. the solution is immediately available with each incoming frame and cannot be changed at any later time.
using public detections This method used the provided detection set as input.
using private detections This method used a private detection set as input.
new This entry has been submitted or updated less than a week ago.

References:


[1] Voigtlaender, P., Krause, M., Osep, A., Luiten, J., Sekar, B.B.G., Geiger, A. & Leibe, B. MOTS: Multi-Object Tracking and Segmentation. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2019.
[2] Ristani, E., Solera, F., Zou, R., Cucchiara, R. & Tomasi, C. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016.
[3] Li, Y., Huang, C. & Nevatia, R. Learning to associate: HybridBoosted multi-target tracker for crowded scene. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2009.



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