Click on a measure to sort the table accordingly. See below for a more detailed description.
Tracker | IDF1 | IDP | IDR | MOTA | MOTP | FAF | MT | ML | FP | FN | ID Sw. | Frag |
MTMC_basel 1. | 91.3 | 91.8 | 90.9 | 90.7 | 78.7 | 0.06 | 1,169 | 14 | 44,492 | 54,044 | 153 | 456 |
Anonymous submission | ||||||||||||
MTMCT_PA 2. | 87.5 | 87.5 | 87.5 | 89.1 | 78.8 | 0.08 | 1,165 | 14 | 57,343 | 57,848 | 229 | 376 |
Anonymous submission | ||||||||||||
d_b_v13 3. | 89.0 | 91.1 | 87.0 | 88.4 | 78.6 | 0.05 | 1,112 | 8 | 37,509 | 84,838 | 724 | 1,954 |
Anonymous submission | ||||||||||||
MTMC_ReID 4. | 89.8 | 92.0 | 87.7 | 88.2 | 79.0 | 0.05 | 1,123 | 17 | 37,911 | 86,958 | 372 | 772 |
Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017. | ||||||||||||
DeepCC 5. | 89.2 | 91.7 | 86.7 | 87.5 | 77.1 | 0.05 | 1,103 | 29 | 37,280 | 94,399 | 202 | 753 |
E. Ristani, C. Tomasi. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR, 2018. | ||||||||||||
MFFusion 6. | 84.9 | 86.6 | 83.4 | 80.9 | 75.6 | 0.11 | 1,079 | 23 | 81,023 | 120,541 | 363 | 8,089 |
Anonymous submission | ||||||||||||
TAREIDMTMC 7. | 83.8 | 87.6 | 80.4 | 83.3 | 75.5 | 0.06 | 1,051 | 17 | 44,691 | 131,220 | 383 | 2,428 |
N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, W. Wu. Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology. In MM, 2018. | ||||||||||||
MOT_TBA 8. | 82.0 | 85.0 | 79.2 | 78.0 | 80.0 | 0.11 | 1,018 | 40 | 79,402 | 152,336 | 803 | 1,466 |
Paper ID 648 | ||||||||||||
SAS_full 9. | 84.0 | 89.4 | 79.2 | 76.0 | 76.0 | 0.09 | 950 | 72 | 66,783 | 186,974 | 169 | 1,256 |
Anonymous submission | ||||||||||||
MYTRACKER 10. | 80.3 | 87.3 | 74.4 | 78.3 | 78.4 | 0.05 | 914 | 72 | 35,580 | 193,253 | 406 | 1,116 |
K. Yoon, Y. Song, M. Jeon. Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views. In IET Image Processing, 2018. | ||||||||||||
Tracker | IDF1 | IDP | IDR | MOTA | MOTP | FAF | MT | ML | FP | FN | ID Sw. | Frag |
SAS 11. | 76.5 | 83.9 | 70.3 | 69.3 | 74.8 | 0.10 | 813 | 89 | 76,059 | 248,224 | 426 | 2,081 |
Submission id 177 | ||||||||||||
MTMC_CDSC 12. | 77.0 | 87.6 | 68.6 | 70.9 | 75.8 | 0.05 | 740 | 110 | 38,655 | 268,398 | 693 | 4,717 |
Y. Tesfaye, E. Zemene, A. Prati, M. Pelillo, M. Shah. Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets. In CoRR, 2017. | ||||||||||||
MTMC_ReIDp 13. | 79.2 | 89.9 | 70.7 | 68.8 | 77.9 | 0.07 | 726 | 143 | 52,408 | 277,762 | 449 | 1,060 |
Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017. | ||||||||||||
MTMC___DS 14. | 70.8 | 84.3 | 61.0 | 65.2 | 75.8 | 0.05 | 603 | 119 | 36,956 | 330,382 | 855 | 4,669 |
Anonymous submission | ||||||||||||
PT_BIPCC 15. | 71.2 | 84.8 | 61.4 | 59.3 | 78.7 | 0.09 | 666 | 234 | 68,634 | 361,589 | 290 | 783 |
A. Maksai, X. Wang, F. Fleuret, P. Fua. Non-Markovian Globally Consistent Multi-Object Tracking. In ICCV, 2017. | ||||||||||||
BIPCC 16. | 70.1 | 83.6 | 60.4 | 59.4 | 78.7 | 0.09 | 665 | 234 | 68,147 | 361,672 | 300 | 801 |
E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016. | ||||||||||||
dirBIPCC 17. | 70.0 | 83.2 | 60.4 | 59.0 | 78.7 | 0.10 | 665 | 234 | 71,381 | 361,673 | 298 | 799 |
Anonymous submission | ||||||||||||
lx_b 18. | 70.3 | 88.1 | 58.5 | 61.3 | 78.7 | 0.04 | 640 | 247 | 26,845 | 382,524 | 246 | 788 |
Anonymous submission |
Tracker | IDF1 | IDP | IDR |
MFFusion 1. | 26.2 | 26.7 | 25.7 |
Anonymous submission | |||
MOT_TBA 2. | 27.8 | 28.8 | 26.8 |
Paper ID 648 | |||
PT_BIPCC 3. | 34.9 | 41.6 | 30.1 |
A. Maksai, X. Wang, F. Fleuret, P. Fua. Non-Markovian Globally Consistent Multi-Object Tracking. In ICCV, 2017. | |||
SAS 4. | 38.5 | 42.3 | 35.4 |
Submission id 177 | |||
dirBIPCC 5. | 52.1 | 62.0 | 45.0 |
Anonymous submission | |||
MTMC___DS 6. | 54.9 | 65.4 | 47.3 |
Anonymous submission | |||
BIPCC 7. | 56.2 | 67.0 | 48.4 |
E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016. | |||
lx_b 8. | 58.0 | 72.6 | 48.2 |
Anonymous submission | |||
SAS_full 9. | 59.9 | 63.8 | 56.5 |
Anonymous submission | |||
MTMC_CDSC 10. | 60.0 | 68.3 | 53.5 |
Y. Tesfaye, E. Zemene, A. Prati, M. Pelillo, M. Shah. Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets. In CoRR, 2017. | |||
Tracker | IDF1 | IDP | IDR |
MYTRACKER 11. | 65.4 | 71.1 | 60.6 |
K. Yoon, Y. Song, M. Jeon. Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views. In IET Image Processing, 2018. | |||
TAREIDMTMC 12. | 68.8 | 71.8 | 66.0 |
N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, W. Wu. Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology. In MM, 2018. | |||
MTMCT_PA 13. | 72.4 | 72.5 | 72.4 |
Anonymous submission | |||
MTMC_ReIDp 14. | 74.4 | 84.4 | 66.4 |
Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017. | |||
DeepCC 15. | 82.0 | 84.4 | 79.8 |
E. Ristani, C. Tomasi. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR, 2018. | |||
MTMC_ReID 16. | 83.2 | 85.2 | 81.2 |
Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017. | |||
d_b_v13 17. | 85.4 | 87.4 | 83.5 |
Anonymous submission | |||
MTMC_basel 18. | 87.4 | 87.8 | 87.0 |
Anonymous submission |
Tracker | IDF1 | IDP | IDR | MOTA | MOTP | FAF | MT | ML | FP | FN | ID Sw. | Frag |
MTMC_basel 1. | 83.7 | 88.8 | 79.1 | 78.9 | 76.6 | 0.13 | 614 | 34 | 35,987 | 114,104 | 307 | 602 |
Anonymous submission | ||||||||||||
MTMCT_PA 2. | 80.3 | 84.9 | 76.2 | 77.5 | 76.6 | 0.15 | 600 | 32 | 43,666 | 116,079 | 469 | 572 |
Anonymous submission | ||||||||||||
d_b_v13 3. | 82.3 | 90.7 | 75.4 | 76.2 | 76.7 | 0.09 | 536 | 38 | 24,319 | 144,813 | 910 | 2,024 |
Anonymous submission | ||||||||||||
MTMC_ReID 4. | 81.2 | 89.4 | 74.5 | 74.7 | 76.6 | 0.11 | 569 | 57 | 30,297 | 149,443 | 521 | 860 |
Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017. | ||||||||||||
MFFusion 5. | 77.5 | 84.5 | 71.5 | 70.9 | 74.5 | 0.17 | 524 | 46 | 48,584 | 158,175 | 660 | 6,556 |
Anonymous submission | ||||||||||||
DeepCC 6. | 79.0 | 87.4 | 72.0 | 70.0 | 75.0 | 0.15 | 524 | 66 | 43,989 | 170,104 | 236 | 777 |
E. Ristani, C. Tomasi. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR, 2018. | ||||||||||||
TAREIDMTMC 7. | 77.9 | 86.6 | 70.7 | 68.1 | 73.5 | 0.17 | 498 | 45 | 47,777 | 179,035 | 541 | 2,074 |
N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, W. Wu. Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology. In MM, 2018. | ||||||||||||
SAS_full 8. | 76.8 | 89.3 | 67.4 | 65.4 | 75.3 | 0.12 | 450 | 87 | 35,596 | 210,639 | 267 | 977 |
Anonymous submission | ||||||||||||
MYTRACKER 9. | 63.5 | 73.9 | 55.6 | 59.6 | 76.7 | 0.19 | 400 | 80 | 55,038 | 231,527 | 1,468 | 1,801 |
K. Yoon, Y. Song, M. Jeon. Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views. In IET Image Processing, 2018. | ||||||||||||
MTMC_ReIDp 10. | 71.6 | 85.3 | 61.7 | 60.9 | 76.8 | 0.14 | 375 | 104 | 40,732 | 237,974 | 572 | 993 |
Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017. | ||||||||||||
Tracker | IDF1 | IDP | IDR | MOTA | MOTP | FAF | MT | ML | FP | FN | ID Sw. | Frag |
SAS 11. | 65.5 | 79.3 | 55.8 | 59.1 | 74.0 | 0.14 | 379 | 102 | 39,576 | 251,256 | 972 | 1,855 |
Submission id 177 | ||||||||||||
MTMC_CDSC 12. | 65.5 | 81.4 | 54.7 | 59.6 | 75.4 | 0.09 | 348 | 99 | 26,643 | 260,073 | 1,637 | 5,024 |
Y. Tesfaye, E. Zemene, A. Prati, M. Pelillo, M. Shah. Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets. In CoRR, 2017. | ||||||||||||
MTMC___DS 13. | 62.6 | 79.4 | 51.6 | 57.3 | 75.3 | 0.09 | 314 | 103 | 26,721 | 276,233 | 1,672 | 4,946 |
Anonymous submission | ||||||||||||
dirBIPCC 14. | 64.3 | 80.3 | 53.6 | 53.9 | 77.1 | 0.16 | 338 | 102 | 45,472 | 283,014 | 653 | 1,078 |
Anonymous submission | ||||||||||||
BIPCC 15. | 64.5 | 81.2 | 53.5 | 54.6 | 77.1 | 0.14 | 338 | 103 | 39,599 | 283,376 | 652 | 1,073 |
E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016. | ||||||||||||
PT_BIPCC 16. | 65.0 | 81.8 | 54.0 | 54.4 | 77.1 | 0.14 | 335 | 104 | 40,978 | 283,704 | 661 | 1,054 |
A. Maksai, X. Wang, F. Fleuret, P. Fua. Non-Markovian Globally Consistent Multi-Object Tracking. In ICCV, 2017. | ||||||||||||
lx_b 17. | 64.2 | 80.4 | 53.4 | 53.6 | 77.1 | 0.16 | 336 | 107 | 45,370 | 285,192 | 621 | 1,049 |
Anonymous submission |
Tracker | IDF1 | IDP | IDR |
MFFusion 1. | 29.7 | 32.3 | 27.4 |
Anonymous submission | |||
PT_BIPCC 2. | 32.9 | 41.3 | 27.3 |
A. Maksai, X. Wang, F. Fleuret, P. Fua. Non-Markovian Globally Consistent Multi-Object Tracking. In ICCV, 2017. | |||
SAS 3. | 33.0 | 40.0 | 28.1 |
Submission id 177 | |||
dirBIPCC 4. | 45.0 | 56.3 | 37.5 |
Anonymous submission | |||
BIPCC 5. | 47.3 | 59.6 | 39.2 |
E. Ristani, F. Solera, R. Zou, R. Cucchiara, C. Tomasi. Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016. | |||
MTMC___DS 6. | 47.6 | 60.4 | 39.3 |
Anonymous submission | |||
lx_b 7. | 48.3 | 60.6 | 40.2 |
Anonymous submission | |||
MYTRACKER 8. | 50.1 | 58.3 | 43.9 |
K. Yoon, Y. Song, M. Jeon. Multiple hypothesis tracking algorithm for multi-target multi-camera tracking with disjoint views. In IET Image Processing, 2018. | |||
MTMC_CDSC 9. | 50.9 | 63.2 | 42.6 |
Y. Tesfaye, E. Zemene, A. Prati, M. Pelillo, M. Shah. Multi-Target Tracking in Multiple Non-Overlapping Cameras using Constrained Dominant Sets. In CoRR, 2017. | |||
SAS_full 10. | 51.7 | 60.1 | 45.4 |
Anonymous submission | |||
Tracker | IDF1 | IDP | IDR |
TAREIDMTMC 11. | 61.2 | 68.0 | 55.5 |
N. Jiang, S. Bai, Y. Xu, C. Xing, Z. Zhou, W. Wu. Online Inter-Camera Trajectory Association Exploiting Person Re-Identification and Camera Topology. In MM, 2018. | |||
MTMCT_PA 12. | 65.5 | 69.2 | 62.2 |
Anonymous submission | |||
MTMC_ReIDp 13. | 65.6 | 78.1 | 56.5 |
Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017. | |||
DeepCC 14. | 68.5 | 75.9 | 62.4 |
E. Ristani, C. Tomasi. Features for Multi-Target Multi-Camera Tracking and Re-Identification. In CVPR, 2018. | |||
MTMC_ReID 15. | 74.0 | 81.4 | 67.8 |
Z. Zhang, J. Wu, X. Zhang, C. Zhang. Multi-Target, Multi-Camera Tracking by Hierarchical Clustering: Recent Progress on DukeMTMC Project. In , 2017. | |||
MTMC_basel 16. | 75.4 | 80.0 | 71.3 |
Anonymous submission | |||
d_b_v13 17. | 78.5 | 86.4 | 71.8 |
Anonymous submission |
Measure | Better | Perfect | Description |
IDF1 | higher | 100 % | ID F1 Score [1]. The ratio of correctly identified detections over the average number of ground-truth and computed detections. |
IDP | higher | 100 % | ID Precision [1]. Identification precision is the fraction of computed detections that are correctly identified. |
IDR | higher | 100 % | ID Recall [1]. Identification recall is the fraction of ground truth detections that are correctly identified. |
MOTA | higher | 100 % | Multiple Object Tracking Accuracy [2]. This measure combines three error sources: false positives, missed targets and identity switches. |
MOTP | higher | 100 % | Multiple Object Tracking Precision [2]. The misalignment between the annotated and the predicted bounding boxes. |
FAF | lower | 0 | The average number of false alarms per frame. |
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. |
FP | lower | 0 | The total number of false positives. |
FN | lower | 0 | The total number of false negatives (missed targets). |
ID Sw. | lower | 0 | The total number of identity switches. Please note that we follow the stricter definition of identity switches as described in [3]. |
Frag | lower | 0 | The total number of times a trajectory is fragmented (i.e. interrupted during tracking). |
Symbol | Description |
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. | |
This method used the provided detection set as input. | |
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This entry has been submitted or updated less than a week ago. |
[1] | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016. |
[2] | Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008. |
[3] | 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. |