MOT16 Results
Click on a measure to sort the table accordingly. See below for a more detailed description.
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| RLMOT 1.  |
74.7 ±13.2 |
73.8 ±10.1 | 81.3 | 322 (42.4) | 131 (17.3) | 8,409 | 36,757 | 79.8 | 94.5 | 1.4 | 963 (12.1) | 2,587 (32.4) | 3.3 | |
| vc_tracker 2.  |
68.2 ±14.7 |
70.8 ±12.1 | 82.3 | 214 (28.2) | 240 (31.6) | 2,698 | 54,834 | 69.9 | 97.9 | 0.5 | 470 (6.7) | 1,300 (18.6) | 22.7 | |
| MAT 3. |
67.7 ±10.5 |
69.6 ±8.5 | 81.0 | 288 (37.9) | 202 (26.6) | 6,337 | 52,234 | 71.4 | 95.4 | 1.1 | 379 (5.3) | 623 (8.7) | 11.5 | |
| MAT: Motion-Aware Multi-Object Tracking | ||||||||||||||
| UIUCTracker 4. |
67.6 ±14.3 |
57.4 ±10.1 | 77.6 | 272 (35.8) | 150 (19.8) | 12,456 | 44,699 | 75.5 | 91.7 | 2.1 | 1,983 (26.3) | 2,218 (29.4) | 7.7 | |
| GNN_tracktor 5. |
65.4 ±14.2 |
59.4 ±11.1 | 78.0 | 215 (28.3) | 225 (29.6) | 5,007 | 55,940 | 69.3 | 96.2 | 0.8 | 2,069 (29.8) | 2,309 (33.3) | 5.5 | |
| Anonymous submission | ||||||||||||||
| CNT 6. |
65.2 ±10.8 |
59.1 ±8.6 | 79.0 | 238 (31.4) | 133 (17.5) | 8,600 | 52,978 | 70.9 | 93.8 | 1.5 | 1,926 (27.1) | 3,706 (52.2) | 11.1 | |
| AOST 7. |
64.8 ±9.9 |
66.7 ±9.1 | 78.8 | 277 (36.5) | 149 (19.6) | 15,084 | 47,941 | 73.7 | 89.9 | 2.5 | 1,092 (14.8) | 2,644 (35.9) | 23.6 | |
| hugmot 8. |
64.5 ±11.7 |
62.8 ±10.3 | 77.9 | 216 (28.5) | 208 (27.4) | 5,344 | 58,625 | 67.8 | 95.9 | 0.9 | 685 (10.1) | 3,582 (52.8) | 30.8 | |
| Multiple Object Tracking by Tracjectory Map Regression with Temporal Priors Embedding | ||||||||||||||
| XJTU 9. |
64.4 ±11.7 |
63.0 ±9.3 | 77.9 | 220 (29.0) | 204 (26.9) | 5,550 | 58,554 | 67.9 | 95.7 | 0.9 | 728 (10.7) | 3,608 (53.1) | 29.6 | |
| Multiple Object Tracking by Trajectory Map Regression | ||||||||||||||
| UnsupTrack 10. |
62.4 ±14.6 |
58.5 ±9.8 | 78.3 | 205 (27.0) | 242 (31.9) | 5,909 | 61,981 | 66.0 | 95.3 | 1.0 | 588 (8.9) | 1,361 (20.6) | 1.9 | |
| S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020. | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| Lif_T 11. |
61.3 ±0.0 |
64.7 ±0.0 | 78.3 | 205 (27.0) | 258 (34.0) | 4,844 | 65,401 | 64.1 | 96.0 | 0.8 | 389 (6.1) | 1,034 (16.1) | 0.5 | |
| A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020. | ||||||||||||||
| ITM 12. |
61.1 ±12.6 |
59.5 ±10.9 | 77.6 | 213 (28.1) | 194 (25.6) | 8,919 | 61,019 | 66.5 | 93.2 | 1.5 | 1,052 (15.8) | 1,933 (29.1) | 1.1 | |
| Pivot Correlation Network with Individualized Tubelets for Efficient Multi-object Tracking | ||||||||||||||
| SLA_public 13. |
60.6 ±9.9 |
59.5 ±8.8 | 78.0 | 184 (24.2) | 221 (29.1) | 5,783 | 65,469 | 64.1 | 95.3 | 1.0 | 643 (10.0) | 1,171 (18.3) | 12.9 | |
| Spatial-Attention Location-Aware Multi-Object Tracking. In , 2020. | ||||||||||||||
| ISE_MOT16 14. |
60.1 ±9.2 |
56.9 ±7.5 | 77.6 | 198 (26.1) | 221 (29.1) | 6,964 | 65,044 | 64.3 | 94.4 | 1.2 | 739 (11.5) | 951 (14.8) | 6.9 | |
| MIFT | ||||||||||||||
| IFA_MOT 15. |
59.8 ±9.4 |
48.0 ±6.6 | 78.3 | 199 (26.2) | 144 (19.0) | 9,118 | 61,617 | 66.2 | 93.0 | 1.5 | 2,599 (39.3) | 4,490 (67.8) | 1.8 | |
| LPC_MOT 16. |
58.8 ±10.3 |
67.6 ±8.8 | 78.4 | 207 (27.3) | 266 (35.0) | 6,167 | 68,432 | 62.5 | 94.9 | 1.0 | 435 (7.0) | 628 (10.1) | 4.3 | |
| Tsinghua University & AIBEE Research. | ||||||||||||||
| MPNTrack 17. |
58.6 ±10.3 |
61.7 ±7.3 | 78.9 | 207 (27.3) | 258 (34.0) | 4,949 | 70,252 | 61.5 | 95.8 | 0.8 | 354 (5.8) | 684 (11.1) | 6.5 | |
| G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020. | ||||||||||||||
| Seed_MOT 18. |
57.7 ±10.5 |
66.1 ±9.2 | 77.7 | 318 (41.9) | 148 (19.5) | 36,735 | 39,281 | 78.5 | 79.6 | 6.2 | 1,099 (14.0) | 1,674 (21.3) | 591.9 | |
| Lif_TsimInt 19. |
57.5 ±9.2 |
64.1 ±6.0 | 79.1 | 193 (25.4) | 263 (34.7) | 4,249 | 72,868 | 60.0 | 96.3 | 0.7 | 335 (5.6) | 604 (10.1) | 5.9 | |
| A. Hornakova, R. Henschel, B. Rosenhahn, P. Swoboda. Lifted Disjoint Paths with Application in Multiple Object Tracking. In ICML, 2020. | ||||||||||||||
| GNNMatch 20. |
57.2 ±11.0 |
55.0 ±10.0 | 79.0 | 174 (22.9) | 258 (34.0) | 3,905 | 73,493 | 59.7 | 96.5 | 0.7 | 559 (9.4) | 847 (14.2) | 0.3 | |
| GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization. ARXIV 2020 | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| GSM_Tracktor 21. |
57.0 ±10.7 |
58.2 ±9.5 | 78.1 | 167 (22.0) | 262 (34.5) | 4,332 | 73,573 | 59.6 | 96.2 | 0.7 | 475 (8.0) | 859 (14.4) | 7.6 | |
| Q. Qiankun Liu, N. Yu. GSM: Graph Similarity Model for Multi-Object Tracking. In IJCAI, 2020. | ||||||||||||||
| FGRNetIV 22. |
56.5 ±11.3 |
55.4 ±9.7 | 79.2 | 156 (20.6) | 269 (35.4) | 2,736 | 75,922 | 58.4 | 97.5 | 0.5 | 611 (10.5) | 1,035 (17.7) | 1.5 | |
| Tracktor++v2 23. |
56.2 ±11.4 |
54.9 ±9.9 | 79.2 | 157 (20.7) | 272 (35.8) | 2,394 | 76,844 | 57.9 | 97.8 | 0.4 | 617 (10.7) | 1,068 (18.5) | 1.6 | |
| P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019. | ||||||||||||||
| TrctrD16 24. |
54.8 ±11.8 |
53.4 ±9.1 | 77.5 | 145 (19.1) | 281 (37.0) | 2,955 | 78,765 | 56.8 | 97.2 | 0.5 | 645 (11.4) | 1,515 (26.7) | 1.6 | |
| Y. Xu, A. Osep, Y. Ban, R. Horaud, L. Leal-Taixe, X. Alameda-Pineda. How To Train Your Deep Multi-Object Tracker. In , 2019. | ||||||||||||||
| VAN_on 25. |
54.6 ±10.9 |
54.2 ±8.2 | 79.4 | 147 (19.4) | 275 (36.2) | 2,307 | 79,895 | 56.2 | 97.8 | 0.4 | 619 (11.0) | 1,574 (28.0) | 8.1 | |
| Tracktor++ 26. |
54.4 ±12.0 |
52.5 ±9.6 | 78.2 | 144 (19.0) | 280 (36.9) | 3,280 | 79,149 | 56.6 | 96.9 | 0.6 | 682 (12.1) | 1,480 (26.2) | 1.5 | |
| P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019. | ||||||||||||||
| HDTR 27. |
53.6 ±8.7 |
46.6 ±6.8 | 80.8 | 161 (21.2) | 281 (37.0) | 4,714 | 79,353 | 56.5 | 95.6 | 0.8 | 618 (10.9) | 833 (14.7) | 3.6 | |
| M. Babaee, A. Athar, G. Rigoll. Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification. In arXiv preprint arXiv:1811.04091, 2018. | ||||||||||||||
| MLT 28. |
52.8 ±8.2 |
62.6 ±6.8 | 76.1 | 160 (21.1) | 322 (42.4) | 5,362 | 80,444 | 55.9 | 95.0 | 0.9 | 299 (5.4) | 702 (12.6) | 5.9 | |
| Y. Zhang, H. Sheng, Y. Wu, S. Wang, W. Ke, Z. Xiong. Multiplex Labeling Graph for Near Online Tracking in Crowded Scenes. In IEEE Internet of Things Journal, 2020. | ||||||||||||||
| TPM 29. |
51.3 ±9.0 |
47.9 ±6.3 | 75.2 | 142 (18.7) | 310 (40.8) | 2,701 | 85,504 | 53.1 | 97.3 | 0.5 | 569 (10.7) | 707 (13.3) | 0.8 | |
| J. Peng, T. Wang, et.al. TPM: Multiple Object Tracking with Tracklet-Plane Matching. In Pattern Recognition, 2020. | ||||||||||||||
| RFS 30. |
50.9 ±11.8 |
53.9 ±11.2 | 73.7 | 127 (16.7) | 298 (39.3) | 8,884 | 79,918 | 56.2 | 92.0 | 1.5 | 714 (12.7) | 1,799 (32.0) | 1.0 | |
| MTSFS：Online Multi-Object Tracking Based on Salient Feature Selection in Crowded Scenes | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| HOMI_Tracker 31. |
50.4 ±12.6 |
47.5 ±8.8 | 77.8 | 170 (22.4) | 232 (30.6) | 18,730 | 69,800 | 61.7 | 85.7 | 3.2 | 1,826 (29.6) | 3,214 (52.1) | 9.9 | |
| PV 32. |
50.4 ±10.3 |
50.8 ±7.7 | 77.7 | 113 (14.9) | 295 (38.9) | 2,600 | 86,780 | 52.4 | 97.4 | 0.4 | 1,061 (20.2) | 3,181 (60.7) | 7.3 | |
| X. S. Li, Y. T. Liu, K. F. Wang. Multi-Target Tracking with Trajectory Prediction and Re-Identification//2019 Chinese Automation Congress. IEEE. | ||||||||||||||
| CRF_TRACK 33. |
50.3 ±7.9 |
54.4 ±6.4 | 74.8 | 139 (18.3) | 271 (35.7) | 7,148 | 82,746 | 54.6 | 93.3 | 1.2 | 702 (12.9) | 1,387 (25.4) | 1.5 | |
| Jun xiang, Chao Ma, Guohan Xu, Jianhua Hou, End-to-End Learning Deep CRF models for Multi-Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2020 | ||||||||||||||
| ENFT16 34. |
50.3 ±8.2 |
55.0 ±6.4 | 76.2 | 146 (19.2) | 302 (39.8) | 8,341 | 81,843 | 55.1 | 92.3 | 1.4 | 490 (8.9) | 754 (13.7) | 0.4 | |
| BUAA | ||||||||||||||
| CMT16 35. |
49.8 ±9.0 |
59.2 ±6.5 | 76.1 | 126 (16.6) | 331 (43.6) | 9,229 | 81,882 | 55.1 | 91.6 | 1.6 | 365 (6.6) | 617 (11.2) | 6.3 | |
| #Submission: TIP-21190-2019 | ||||||||||||||
| NOTA 36. |
49.8 ±8.3 |
55.3 ±5.4 | 74.5 | 136 (17.9) | 286 (37.7) | 7,248 | 83,614 | 54.1 | 93.2 | 1.2 | 614 (11.3) | 1,372 (25.3) | 19.2 | |
| L. Chen, H. Ai, R. Chen, Z. Zhuang. Aggregate Tracklet Appearance Features for Multi-Object Tracking. In IEEE Signal Processing Letters, 2019. | ||||||||||||||
| siameseCos 37. |
49.4 ±8.1 |
49.8 ±7.4 | 75.9 | 145 (19.1) | 299 (39.4) | 6,281 | 85,384 | 53.2 | 93.9 | 1.1 | 679 (12.8) | 823 (15.5) | 0.8 | |
| In preparation | ||||||||||||||
| HCC 38. |
49.3 ±10.2 |
50.7 ±7.4 | 79.0 | 135 (17.8) | 303 (39.9) | 5,333 | 86,795 | 52.4 | 94.7 | 0.9 | 391 (7.5) | 535 (10.2) | 0.8 | |
| L. Ma, S. Tang, M. Black, L. Gool. Customized Multi-Person Tracker. In Computer Vision -- ACCV 2018, 2018. | ||||||||||||||
| LSST16O 39. |
49.2 ±10.2 |
56.5 ±7.2 | 74.0 | 102 (13.4) | 314 (41.4) | 7,187 | 84,875 | 53.4 | 93.1 | 1.2 | 606 (11.3) | 2,497 (46.7) | 2.0 | |
| Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification | ||||||||||||||
| eTC 40. |
49.2 ±9.1 |
56.1 ±7.2 | 75.5 | 131 (17.3) | 306 (40.3) | 8,400 | 83,702 | 54.1 | 92.2 | 1.4 | 606 (11.2) | 882 (16.3) | 0.7 | |
| G. Wang, Y. Wang, H. Zhang, R. Gu, J. Hwang. Exploit the connectivity: Multi-object tracking with trackletnet. In Proceedings of the 27th ACM International Conference on Multimedia, 2019. | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| AFN 41. |
49.0 ±10.2 |
48.2 ±7.4 | 78.0 | 145 (19.1) | 271 (35.7) | 9,508 | 82,506 | 54.7 | 91.3 | 1.6 | 899 (16.4) | 1,383 (25.3) | 0.6 | |
| H. Shen, L. Huang, C. Huang, W. Xu. Tracklet Association Tracker: An End-to-End Learning-based Association Approach for Multi-Object Tracking. In CoRR, 2018. | ||||||||||||||
| KCF16 42. |
48.8 ±9.6 |
47.2 ±7.8 | 75.7 | 120 (15.8) | 289 (38.1) | 5,875 | 86,567 | 52.5 | 94.2 | 1.0 | 906 (17.3) | 1,116 (21.2) | 0.1 | |
| P. Chu, H. Fan, C. Tan, H. Ling. Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment. In WACV, 2019. | ||||||||||||||
| LMP 43. |
48.8 ±8.9 |
51.3 ±6.8 | 79.0 | 138 (18.2) | 304 (40.1) | 6,654 | 86,245 | 52.7 | 93.5 | 1.1 | 481 (9.1) | 595 (11.3) | 0.5 | |
| S. Tang, M. Andriluka, B. Andres, B. Schiele. Multiple People Tracking with Lifted Multicut and Person Re-identification. In CVPR, 2017. | ||||||||||||||
| TLMHT 44. |
48.7 ±8.6 |
55.3 ±5.6 | 76.4 | 119 (15.7) | 338 (44.5) | 6,632 | 86,504 | 52.6 | 93.5 | 1.1 | 413 (7.9) | 642 (12.2) | 4.8 | |
| H. Sheng, J. Chen, Y. Zhang, W. Ke, Z. Xiong, J. Yu. Iterative Multiple Hypothesis Tracking with Tracklet-level Association. In IEEE Transactions on Circuits and Systems for Video Technology, 2018. | ||||||||||||||
| STRN_MOT16 45. |
48.5 ±8.5 |
53.9 ±7.1 | 73.7 | 129 (17.0) | 265 (34.9) | 9,038 | 84,178 | 53.8 | 91.6 | 1.5 | 747 (13.9) | 2,919 (54.2) | 13.5 | |
| J. Xu, Y. Cao, Z. Zhang, H. Hu. Spatial-Temporal Relation Networks for Multi-Object Tracking. In ICCV, 2019. | ||||||||||||||
| TSN 46. |
48.2 ±8.6 |
45.7 ±7.6 | 75.0 | 151 (19.9) | 295 (38.9) | 8,447 | 85,315 | 53.2 | 92.0 | 1.4 | 665 (12.5) | 829 (15.6) | 0.8 | |
| J. Peng, F. Qiu, et.al. Tracklet Siamese Network with Constrained Clustering for Multiple Object Tracking. In VCIP, 2018. | ||||||||||||||
| GCRA 47. |
48.2 ±8.3 |
48.6 ±5.6 | 77.5 | 98 (12.9) | 312 (41.1) | 5,104 | 88,586 | 51.4 | 94.8 | 0.9 | 821 (16.0) | 1,117 (21.7) | 2.8 | |
| C. Ma, C. Yang, F. Yang, Y. Zhuang, Z. Zhang, H. Jia, X. Xie. Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking. In ICME, 2018. | ||||||||||||||
| FWT 48. |
47.8 ±10.0 |
44.3 ±11.0 | 75.5 | 145 (19.1) | 290 (38.2) | 8,886 | 85,487 | 53.1 | 91.6 | 1.5 | 852 (16.0) | 1,534 (28.9) | 0.6 | |
| R. Henschel, L. Leal-Taixé, D. Cremers, B. Rosenhahn. Fusion of Head and Full-Body Detectors for Multi-Object Tracking. In Trajnet CVPRW, 2018. | ||||||||||||||
| MOTDT 49. |
47.6 ±8.4 |
50.9 ±5.5 | 74.8 | 115 (15.2) | 291 (38.3) | 9,253 | 85,431 | 53.1 | 91.3 | 1.6 | 792 (14.9) | 1,858 (35.0) | 20.6 | |
| C. Long, A. Haizhou, Z. Zijie, S. Chong. Real-time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-identification. In ICME, 2018. | ||||||||||||||
| NLLMPa 50. |
47.6 ±10.6 |
47.3 ±9.6 | 78.5 | 129 (17.0) | 307 (40.4) | 5,844 | 89,093 | 51.1 | 94.1 | 1.0 | 629 (12.3) | 768 (15.0) | 8.3 | |
| E. Levinkov, J. Uhrig, S. Tang, M. Omran, E. Insafutdinov, A. Kirillov, C. Rother, T. Brox, B. Schiele, B. Andres. Joint Graph Decomposition and Node Labeling: Problem, Algorithms, Applications. In CVPR, 2017. | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| EAGS16 51. |
47.4 ±8.6 |
50.1 ±7.4 | 75.9 | 131 (17.3) | 324 (42.7) | 8,369 | 86,931 | 52.3 | 91.9 | 1.4 | 575 (11.0) | 913 (17.5) | 197.3 | |
| H. Sheng, X. Zhang, Y. Zhang, Y. Wu, J. Chen. Enhanced Association with Supervoxels in Multiple Hypothesis Tracking. In IEEE Access, 2018. | ||||||||||||||
| JCSTD 52. |
47.4 ±8.3 |
41.1 ±5.6 | 74.4 | 109 (14.4) | 276 (36.4) | 8,076 | 86,638 | 52.5 | 92.2 | 1.4 | 1,266 (24.1) | 2,697 (51.4) | 8.8 | |
| W. Tian, M. Lauer, L. Chen. Online Multi-Object Tracking Using Joint Domain Information in Traffic Scenarios. In IEEE Transactions on Intelligent Transportation Systems, 2019. | ||||||||||||||
| ASTT 53. |
47.2 ±9.6 |
44.3 ±8.5 | 76.1 | 124 (16.3) | 316 (41.6) | 4,680 | 90,877 | 50.2 | 95.1 | 0.8 | 633 (12.6) | 814 (16.2) | 0.5 | |
| Yi Tao el al., “Adaptive Spatio-temporal Model Based Multiple Object Tracking Considering a Moving Camera[C]”, International Conference on Universal Village (UV), 2018. | ||||||||||||||
| eHAF16 54. |
47.2 ±8.7 |
52.4 ±7.6 | 75.7 | 141 (18.6) | 325 (42.8) | 12,586 | 83,107 | 54.4 | 88.7 | 2.1 | 542 (10.0) | 787 (14.5) | 0.5 | |
| H. Sheng, Y. Zhang, J. Chen, Z. Xiong, J. Zhang. Heterogeneous Association Graph Fusion for Target Association in Multiple Object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018. | ||||||||||||||
| AMIR 55. |
47.2 ±8.2 |
46.3 ±7.2 | 75.8 | 106 (14.0) | 316 (41.6) | 2,681 | 92,856 | 49.1 | 97.1 | 0.5 | 774 (15.8) | 1,675 (34.1) | 1.0 | |
| A. Sadeghian, A. Alahi, S. Savarese. Tracking The Untrackable: Learning To Track Multiple Cues with Long-Term Dependencies. In ICCV, 2017. | ||||||||||||||
| MCjoint 56. |
47.1 ±10.3 |
52.3 ±10.2 | 76.3 | 155 (20.4) | 356 (46.9) | 6,703 | 89,368 | 51.0 | 93.3 | 1.1 | 370 (7.3) | 598 (11.7) | 0.6 | |
| }@article{DBLP:journals/corr/KeuperTYABS16, author = {Margret Keuper and Siyu Tang and Zhongjie Yu and Bjoern Andres and Thomas Brox and Bernt Schiele}, title = {A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects}, journal = {CoRR}, volume = {abs/1607.06317}, year = {2016}, url = {http://arxiv.org/abs/1607.06317}, timestamp = {Wed, 07 Jun 2017 14:41:31 +0200}, biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/KeuperTYABS16}, bibsource = {dblp computer science bibliography, http://dblp.org} } | ||||||||||||||
| YOONKJ16 57. |
47.0 ±8.1 |
50.1 ±7.5 | 75.8 | 125 (16.5) | 317 (41.8) | 7,901 | 88,179 | 51.6 | 92.3 | 1.3 | 627 (12.1) | 945 (18.3) | 3.5 | |
| K. YOON, J. GWAK, Y. SONG, Y. YOON, M. JEON. OneShotDA: Online Multi-object Tracker with One-shot-learning-based Data Association. In IEEE Access, 2020. | ||||||||||||||
| CS_MOT 58. |
46.7 ±10.6 |
51.5 ±10.9 | 74.0 | 76 (10.0) | 332 (43.7) | 5,941 | 90,566 | 50.3 | 93.9 | 1.0 | 619 (12.3) | 2,981 (59.2) | 1.2 | |
| A Cost Matrix Optimization Method Based on Spatial Constraints under Hungarian Algorithm | ||||||||||||||
| NOMT 59. |
46.4 ±8.9 |
53.3 ±7.5 | 76.6 | 139 (18.3) | 314 (41.4) | 9,753 | 87,565 | 52.0 | 90.7 | 1.6 | 359 (6.9) | 504 (9.7) | 2.6 | |
| W. Choi. Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor. In ICCV, 2015. | ||||||||||||||
| JMC 60. |
46.3 ±9.4 |
46.3 ±9.2 | 75.7 | 118 (15.5) | 301 (39.7) | 6,373 | 90,914 | 50.1 | 93.5 | 1.1 | 657 (13.1) | 1,114 (22.2) | 0.8 | |
| S. Tang, B. Andres, M. Andriluka, B. Schiele. Multi-Person Tracking by Multicuts and Deep Matching. In BMTT, 2016. | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| DD_TAMA16 61. |
46.2 ±8.4 |
49.4 ±7.6 | 75.4 | 107 (14.1) | 334 (44.0) | 5,126 | 92,367 | 49.3 | 94.6 | 0.9 | 598 (12.1) | 1,127 (22.8) | 6.5 | |
| Y. Yoon, D. Kim, Y. Song, K. Yoon, M. Jeon. Online Multiple Pedestrians Tracking using Deep Temporal Appearance Matching Association. In Information Sciences, 2020. | ||||||||||||||
| DASOT16 62. |
46.1 ±9.2 |
49.4 ±8.8 | 75.3 | 111 (14.6) | 316 (41.6) | 8,222 | 89,204 | 51.1 | 91.9 | 1.4 | 802 (15.7) | 2,057 (40.3) | 9.0 | |
| Q. Chu, W. Ouyang, B. Liu, F. Zhu, N. Yu. DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020. | ||||||||||||||
| DMAN 63. |
46.1 ±9.1 |
54.8 ±7.0 | 73.8 | 132 (17.4) | 324 (42.7) | 7,909 | 89,874 | 50.7 | 92.1 | 1.3 | 532 (10.5) | 1,616 (31.9) | 0.3 | |
| J. Zhu, H. Yang, N. Liu, M. Kim, W. Zhang, M. Yang. Online Multi-Object Tracking with Dual Matching Attention Networks. In ECCV, 2018. | ||||||||||||||
| EDR16 64. |
46.1 ±8.3 |
46.2 ±6.0 | 77.1 | 106 (14.0) | 289 (38.1) | 4,418 | 92,849 | 49.1 | 95.3 | 0.7 | 1,061 (21.6) | 3,102 (63.2) | 19.7 | |
| Z. Fu, X. Lai, S. Naqvi. Enhanced Detection Reliability for Human Tracking Based Video Analytics. In International Conference on Information Fusion (FUSION), 2019. | ||||||||||||||
| STAM16 65. |
46.0 ±9.1 |
50.0 ±8.4 | 74.9 | 111 (14.6) | 331 (43.6) | 6,895 | 91,117 | 50.0 | 93.0 | 1.2 | 473 (9.5) | 1,422 (28.4) | 0.2 | |
| Q. Chu, W. Ouyang, H. Li, X. Wang, B. Liu, N. Yu. Online Multi-object Tracking Using CNN-Based Single Object Tracker with Spatial-Temporal Attention Mechanism. In 2017 IEEE International Conference on Computer Vision (ICCV), 2017. | ||||||||||||||
| deepS2 66. |
46.0 ±8.2 |
46.5 ±6.1 | 76.3 | 118 (15.5) | 323 (42.6) | 5,124 | 92,697 | 49.2 | 94.6 | 0.9 | 693 (14.1) | 759 (15.4) | 0.7 | |
| ID 32 | ||||||||||||||
| RAR16pub 67. |
45.9 ±9.7 |
48.8 ±7.6 | 74.8 | 100 (13.2) | 318 (41.9) | 6,871 | 91,173 | 50.0 | 93.0 | 1.2 | 648 (13.0) | 1,992 (39.8) | 0.9 | |
| K. Fang, Y. Xiang, X. Li, S. Savarese. Recurrent Autoregressive Networks for Online Multi-Object Tracking. In The IEEE Winter Conference on Applications of Computer Vision (WACV), 2018. | ||||||||||||||
| MHT_DAM 68. |
45.8 ±8.6 |
46.1 ±7.6 | 76.3 | 123 (16.2) | 328 (43.2) | 6,412 | 91,758 | 49.7 | 93.4 | 1.1 | 590 (11.9) | 781 (15.7) | 0.8 | |
| C. Kim, F. Li, A. Ciptadi, J. Rehg. Multiple Hypothesis Tracking Revisited. In ICCV, 2015. | ||||||||||||||
| MTDF 69. |
45.7 ±10.8 |
40.1 ±8.7 | 72.6 | 107 (14.1) | 276 (36.4) | 12,018 | 84,970 | 53.4 | 89.0 | 2.0 | 1,987 (37.2) | 3,377 (63.2) | 1.5 | |
| Z. Fu, F. Angelini, J. Chambers, S. Naqvi. Multi-Level Cooperative Fusion of GM-PHD Filters for Online Multiple Human Tracking. In IEEE Transactions on Multimedia, 2019. | ||||||||||||||
| INTERA_MOT 70. |
45.4 ±8.1 |
47.7 ±9.0 | 74.4 | 137 (18.1) | 294 (38.7) | 13,407 | 85,547 | 53.1 | 87.8 | 2.3 | 600 (11.3) | 930 (17.5) | 4.3 | |
| L. Lan, X. Wang, S. Zhang, D. Tao, W. Gao, T. Huang. Interacting Tracklets for Multi-object Tracking. In IEEE Transactions on Image Processing, 2018. | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| EDMT 71. |
45.3 ±9.1 |
47.9 ±7.8 | 75.9 | 129 (17.0) | 303 (39.9) | 11,122 | 87,890 | 51.8 | 89.5 | 1.9 | 639 (12.3) | 946 (18.3) | 1.8 | |
| J. Chen, H. Sheng, Y. Zhang, Z. Xiong. Enhancing Detection Model for Multiple Hypothesis Tracking. In BMTT-PETS CVPRw, 2017. | ||||||||||||||
| DCCRF16 72. |
44.8 ±9.5 |
39.7 ±8.2 | 75.6 | 107 (14.1) | 321 (42.3) | 5,613 | 94,133 | 48.4 | 94.0 | 0.9 | 968 (20.0) | 1,378 (28.5) | 0.1 | |
| H. Zhou, W. Ouyang, J. Cheng, X. Wang, H. Li. Deep Continuous Conditional Random Fields with Asymmetric Inter-object Constraints for Online Multi-object Tracking. In IEEE Transactions on Circuits and Systems for Video Technology, 2018. | ||||||||||||||
| TBSS 73. |
44.6 ±9.3 |
42.6 ±7.2 | 75.2 | 93 (12.3) | 333 (43.9) | 4,136 | 96,128 | 47.3 | 95.4 | 0.7 | 790 (16.7) | 1,419 (30.0) | 3.0 | |
| X. Zhou, P. Jiang, Z. Wei, H. Dong, F. Wang. Online Multi-Object Tracking with Structural Invariance Constraint. In BMVC, 2018. | ||||||||||||||
| OTCD_1 74. |
44.4 ±10.8 |
45.6 ±10.4 | 75.4 | 88 (11.6) | 361 (47.6) | 5,759 | 94,927 | 47.9 | 93.8 | 1.0 | 759 (15.8) | 1,787 (37.3) | 17.6 | |
| Q. Liu, B. Liu, Y. Wu, W. Li, N. Yu. Real-Time Online Multi-Object Tracking in Compressed Domain. In IEEE Access, 2019. | ||||||||||||||
| QuadMOT16 75. |
44.1 ±9.4 |
38.3 ±8.6 | 76.4 | 111 (14.6) | 341 (44.9) | 6,388 | 94,775 | 48.0 | 93.2 | 1.1 | 745 (15.5) | 1,096 (22.8) | 1.8 | |
| J. Son, M. Baek, M. Cho, B. Han. Multi-Object Tracking with Quadruplet Convolutional Neural Networks. In CVPR, 2017. | ||||||||||||||
| CDA_DDALv2 76. |
43.9 ±7.8 |
45.1 ±5.7 | 74.7 | 81 (10.7) | 337 (44.4) | 6,450 | 95,175 | 47.8 | 93.1 | 1.1 | 676 (14.1) | 1,795 (37.6) | 0.5 | |
| S. Bae and K. Yoon, Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking , In IEEE TPAMI, 2017. | ||||||||||||||
| LFNF16 77. |
43.6 ±8.4 |
41.6 ±7.8 | 76.6 | 101 (13.3) | 347 (45.7) | 6,616 | 95,363 | 47.7 | 92.9 | 1.1 | 836 (17.5) | 938 (19.7) | 0.6 | |
| Sheng H, Hao L, Chen J, et al. Robust Local Effective Matching Model for Multi-Target Tracking. In PCM, 2017 | ||||||||||||||
| oICF 78. |
43.2 ±10.6 |
49.3 ±9.1 | 74.3 | 86 (11.3) | 368 (48.5) | 6,651 | 96,515 | 47.1 | 92.8 | 1.1 | 381 (8.1) | 1,404 (29.8) | 0.4 | |
| H. Kieritz, S. Becker, W. Hübner, M. Arens. Online Multi-Person Tracking using Integral Channel Features. In IEEE Advanced Video and Signal-based Surveillance (AVSS) 2016, 2016. | ||||||||||||||
| MHT_bLSTM6 79. |
42.1 ±8.6 |
47.8 ±7.4 | 75.9 | 113 (14.9) | 337 (44.4) | 11,637 | 93,172 | 48.9 | 88.5 | 2.0 | 753 (15.4) | 1,156 (23.6) | 1.8 | |
| C. Kim, F. Li, J. Rehg. Multi-object Tracking with Neural Gating Using Bilinear LSTM. In ECCV, 2018. | ||||||||||||||
| TestUnsup 80. |
41.5 ±9.0 |
44.9 ±8.8 | 75.2 | 104 (13.7) | 330 (43.5) | 12,596 | 93,404 | 48.8 | 87.6 | 2.1 | 643 (13.2) | 796 (16.3) | 19.7 | |
| Multi Object Tracking using Deep Structural Cost Minimization in Data Association | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| LINF1 81. |
41.0 ±10.1 |
45.7 ±8.5 | 74.8 | 88 (11.6) | 389 (51.3) | 7,896 | 99,224 | 45.6 | 91.3 | 1.3 | 430 (9.4) | 963 (21.1) | 4.2 | |
| L. Fagot-Bouquet, R. Audigier, Y. Dhome, F. Lerasle. Improving Multi-Frame Data Association with Sparse Representations for Robust Near-Online Multi-Object Tracking. In ECCV, 2016. | ||||||||||||||
| PHD_GSDL16 82. |
41.0 ±8.9 |
43.1 ±6.9 | 75.9 | 86 (11.3) | 315 (41.5) | 6,498 | 99,257 | 45.6 | 92.7 | 1.1 | 1,810 (39.7) | 3,650 (80.1) | 8.3 | |
| Z. Fu, P. Feng, F. Angelini, J. Chambers, S. Naqvi. Particle PHD Filter based Multiple Human Tracking using Online Group-Structured Dictionary Learning. In IEEE Access, 2018. | ||||||||||||||
| GMPHD_ReId 83. |
40.4 ±9.3 |
50.1 ±8.6 | 75.2 | 87 (11.5) | 327 (43.1) | 6,569 | 101,251 | 44.5 | 92.5 | 1.1 | 789 (17.7) | 2,519 (56.6) | 31.6 | |
| N. Baisa. Occlusion-robust Online Multi-object Visual Tracking using a GM-PHD Filter with a CNN-based Re-identification. In , 2019. | ||||||||||||||
| PMPTracker 84. |
40.3 ±11.7 |
38.2 ±8.4 | 73.3 | 79 (10.4) | 319 (42.0) | 10,071 | 97,524 | 46.5 | 89.4 | 1.7 | 1,343 (28.9) | 2,764 (59.4) | 148.0 | |
| Light version of PTZ camera Mutiple People Tracker | ||||||||||||||
| AM_ADM 85. |
40.1 ±10.1 |
43.8 ±9.6 | 75.4 | 54 (7.1) | 351 (46.2) | 8,503 | 99,891 | 45.2 | 90.6 | 1.4 | 789 (17.5) | 1,736 (38.4) | 5.8 | |
| S. Lee, M. Kim, S. Bae, Learning Discriminative Appearance Models for Online Multi-Object Tracking with Appearance Discriminability Measures, In IEEE Access, 2018. | ||||||||||||||
| SDMT 86. |
39.6 ±8.1 |
42.3 ±6.6 | 75.5 | 89 (11.7) | 373 (49.1) | 11,130 | 98,343 | 46.1 | 88.3 | 1.9 | 602 (13.1) | 772 (16.8) | 19.8 | |
| M. Thoreau, N. Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In arXiv, 2018. | ||||||||||||||
| CDF17 87. |
39.3 ±10.4 |
33.6 ±9.9 | 74.8 | 95 (12.5) | 310 (40.8) | 12,430 | 93,394 | 48.8 | 87.7 | 2.1 | 4,934 (101.2) | 5,886 (120.7) | 9.7 | |
| Z. Fu, S. Naqvi, J. Chambers. Collaborative Detector Fusion of Data-Driven PHD Filter for Online Multiple Human Tracking. In 2018 21st International Conference on Information Fusion (FUSION), 2018. | ||||||||||||||
| EAMTT_pub 88. |
38.8 ±8.6 |
42.4 ±7.4 | 75.1 | 60 (7.9) | 373 (49.1) | 8,114 | 102,452 | 43.8 | 90.8 | 1.4 | 965 (22.0) | 1,657 (37.8) | 11.8 | |
| R. Sanchez-Matilla, F. Poiesi, A. Cavallaro "Multi-target tracking with strong and weak detections" in BMTT ECCVw 2016 | ||||||||||||||
| OVBT 89. |
38.4 ±8.6 |
37.8 ±5.5 | 75.4 | 57 (7.5) | 359 (47.3) | 11,517 | 99,463 | 45.4 | 87.8 | 1.9 | 1,321 (29.1) | 2,140 (47.1) | 0.3 | |
| Y. Ban, S. Ba, X. Alameda-Pineda, R. Horaud. Tracking Multiple Persons Based on a Variational Bayesian Model. In BMTT 2016, . | ||||||||||||||
| GMMCP 90. |
38.1 ±7.8 |
35.5 ±4.5 | 75.8 | 65 (8.6) | 386 (50.9) | 6,607 | 105,315 | 42.2 | 92.1 | 1.1 | 937 (22.2) | 1,669 (39.5) | 0.5 | |
| A. Dehghan, S. Assari, M. Shah.. GMMCP-Tracker:Globally Optimal Generalized Maximum Multi Clique Problem for Multiple Object Tracking. In CVPR, 2015. | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| LTTSC-CRF 91. |
37.6 ±8.0 |
42.1 ±6.6 | 75.9 | 73 (9.6) | 419 (55.2) | 11,969 | 101,343 | 44.4 | 87.1 | 2.0 | 481 (10.8) | 1,012 (22.8) | 0.6 | |
| N. Le, A. Heili, M. Odobez. Long-Term Time-Sensitive Costs for CRF-Based Tracking by Detection. In ECCVw, 2016. | ||||||||||||||
| HISP_DAL 92. |
37.4 ±8.8 |
30.5 ±6.8 | 76.3 | 58 (7.6) | 386 (50.9) | 3,222 | 108,865 | 40.3 | 95.8 | 0.5 | 2,101 (52.1) | 2,151 (53.4) | 3.3 | |
| N. Baisa. Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning. In CoRR, 2019. | ||||||||||||||
| JCmin_MOT 93. |
36.7 ±9.1 |
36.2 ±10.5 | 75.9 | 57 (7.5) | 413 (54.4) | 2,936 | 111,890 | 38.6 | 96.0 | 0.5 | 667 (17.3) | 831 (21.5) | 14.8 | |
| M. Abhijeet Boragule. Joint Cost Minimization for Multi-Object Tracking. In 2017 IEEE International Conference on Advanced Vide and Signale Based Surveillance, 2017. | ||||||||||||||
| HISP_T 94. |
35.9 ±8.7 |
28.9 ±0.0 | 76.1 | 59 (7.8) | 380 (50.1) | 6,412 | 107,918 | 40.8 | 92.1 | 1.1 | 2,594 (63.6) | 2,298 (56.3) | 4.8 | |
| N. Baisa. Online Multi-target Visual Tracking using a HISP Filter. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,, 2018. | ||||||||||||||
| LP2D 95. |
35.7 ±12.3 |
34.2 ±9.3 | 75.8 | 66 (8.7) | 385 (50.7) | 5,084 | 111,163 | 39.0 | 93.3 | 0.9 | 915 (23.4) | 1,264 (32.4) | 49.3 | |
| MOT baseline: Linear programming on 2D image coordinates. | ||||||||||||||
| GM_PHD_DAL 96. |
35.1 ±9.1 |
26.6 ±7.1 | 76.6 | 53 (7.0) | 390 (51.4) | 2,350 | 111,886 | 38.6 | 96.8 | 0.4 | 4,047 (104.8) | 5,338 (138.2) | 3.5 | |
| N. Baisa. Online Multi-object Visual Tracking using a GM-PHD Filter with Deep Appearance Learning. In 2019 22th International Conference on Information Fusion (FUSION), 2019. | ||||||||||||||
| TBD 97. |
33.7 ±8.8 |
0.0 ±0.0 | 76.5 | 55 (7.2) | 411 (54.2) | 5,804 | 112,587 | 38.2 | 92.3 | 1.0 | 2,418 (63.2) | 2,252 (58.9) | 1.3 | |
| A. Geiger, M. Lauer, C. Wojek, C. Stiller, R. Urtasun. 3D Traffic Scene Understanding from Movable Platforms. In Pattern Analysis and Machine Intelligence (PAMI), 2014. | ||||||||||||||
| GM_PHD_N1T 98. |
33.3 ±9.0 |
25.5 ±8.2 | 76.8 | 42 (5.5) | 425 (56.0) | 1,750 | 116,452 | 36.1 | 97.4 | 0.3 | 3,499 (96.8) | 3,594 (99.5) | 9.9 | |
| N. Baisa, A. Wallace. Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking. In Journal of Visual Communication and Image Representation, 2019. | ||||||||||||||
| CEM 99. |
33.2 ±8.8 |
0.0 ±0.0 | 75.8 | 59 (7.8) | 413 (54.4) | 6,837 | 114,322 | 37.3 | 90.9 | 1.2 | 642 (17.2) | 731 (19.6) | 5,919.0 | |
| A. Milan, S. Roth, K. Schindler. Continuous Energy Minimization for Multitarget Tracking. In IEEE TPAMI, 2014. | ||||||||||||||
| CppSORT 100. |
31.5 ±9.0 |
27.7 ±9.7 | 77.3 | 33 (4.3) | 455 (59.9) | 3,048 | 120,278 | 34.0 | 95.3 | 0.5 | 1,587 (46.6) | 2,239 (65.8) | 687.1 | |
| S. Murray. Real-Time Multiple Object Tracking - A Study on the Importance of Speed. In arXiv preprint arXiv:1709.03572, 2017. | ||||||||||||||
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| LM_NN 101. |
31.0 ±7.2 |
31.5 ±8.3 | 78.4 | 56 (7.4) | 443 (58.4) | 2,451 | 122,649 | 32.7 | 96.1 | 0.4 | 678 (20.7) | 666 (20.3) | 3.0 | |
| ID NEUCOM-D-18-03230 | ||||||||||||||
| GMPHD_HDA 102. |
30.5 ±6.9 |
33.4 ±5.4 | 75.4 | 35 (4.6) | 453 (59.7) | 5,169 | 120,970 | 33.6 | 92.2 | 0.9 | 539 (16.0) | 731 (21.7) | 13.6 | |
| Y. Song, M. Jeon. Online Multiple Object Tracking with the Hierarchically Adopted GM-PHD Filter using Motion and Appearance. In IEEE/IEIE The International Conference on Consumer Electronics (ICCE) Asia, 2016. | ||||||||||||||
| SMOT 103. |
29.7 ±8.8 |
0.0 ±0.0 | 75.2 | 40 (5.3) | 362 (47.7) | 17,426 | 107,552 | 41.0 | 81.1 | 2.9 | 3,108 (75.8) | 4,483 (109.3) | 0.2 | |
| C. Dicle, O. Camps, M. Sznaier. The Way They Move: Tracking Targets with Similar Appearance. In ICCV, 2013. | ||||||||||||||
| JPDA_m 104. |
26.2 ±8.8 |
0.0 ±0.0 | 76.3 | 31 (4.1) | 512 (67.5) | 3,689 | 130,549 | 28.4 | 93.3 | 0.6 | 365 (12.9) | 638 (22.5) | 22.2 | |
| H. Rezatofighi, A. Milan, Z. Zhang, Q. Shi, A. Dick, I. Reid. Joint Probabilistic Data Association Revisited. In ICCV, 2015. | ||||||||||||||
| DP_NMS 105. |
26.2 ±9.7 |
31.2 ±5.1 | 76.3 | 31 (4.1) | 512 (67.5) | 3,689 | 130,557 | 28.4 | 93.3 | 0.6 | 365 (12.9) | 638 (22.5) | 212.6 | |
| H. Pirsiavash, D. Ramanan, C. Fowlkes. Globally-Optimal Greedy Algorithms for Tracking a Variable Number of Objects. In CVPR, 2011. | ||||||||||||||
| Fa_m 106. |
-15.9 ±112.9 |
21.2 ±28.5 | 80.5 | 152 (20.0) | 398 (52.4) | 66,233 | 144,576 | 20.7 | 36.3 | 11.2 | 522 (25.2) | 1,355 (65.4) | 33.8 | |
| Sequences | Frames | Trajectories | Boxes |
| 7 | 5919 | 759 | 182326 |
Difficulty Analysis
Sequence difficulty (from easiest to hardest, measured by average MOTA)
MOT16-03
(55.5 MOTA)
MOT16-06
(46.4 MOTA)
MOT16-12
(42.1 MOTA)
...
...
MOT16-08
(33.0 MOTA)
MOT16-14
(23.5 MOTA)
Evaluation Measures
Lower is better. Higher is better.| Measure | Better | Perfect | Description |
| MOTA | higher | 100% | Multi-Object Tracking Accuracy (+/- denotes standard deviation across all sequences) [1]. This measure combines three error sources: false positives, missed targets and identity switches. |
| IDF1 | higher | 100% | ID F1 Score [2]. The ratio of correctly identified detections over the average number of ground-truth and computed detections. |
| MOTP | higher | 100% | Multi-Object Tracking Precision (+/- denotes standard deviation across all sequences) [1]. The misalignment between the annotated and the predicted bounding boxes. |
| 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). |
| Recall | higher | 100% | Ratio of correct detections to total number of GT boxes. |
| Precision | higher | 100% | Ratio of TP / (TP+FP). |
| FAF | lower | 0 | The average number of false alarms per frame. |
| ID Sw. | lower | 0 | Number 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 | 0 | The 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 |
|
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. |
 |
This method used a private detection set as input. |
 |
This entry has been submitted or updated less than a week ago. |
References:
| [1] | Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008. |
| [2] | Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. In ECCV workshop on Benchmarking Multi-Target Tracking, 2016. |
| [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. |
