MOT20 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 | |
| Tracktor++v2 1.   |
52.6 ±19.9 |
52.7 ±14.9 | 79.9 | 365 (29.4) | 331 (26.7) | 6,930 | 236,680 | 54.3 | 97.6 | 1.5 | 1,648 (30.4) | 4,374 (80.6) | 1.2 | |
| P. Bergmann, T. Meinhardt, L. Leal-Taixé. Tracking without bells and whistles. In ICCV, 2019. | ||||||||||||||
| SORT20 2. |
42.7 ±18.6 |
45.1 ±13.1 | 78.5 | 208 (16.7) | 326 (26.2) | 27,521 | 264,694 | 48.8 | 90.2 | 6.1 | 4,470 (91.5) | 17,798 (364.4) | 57.3 | |
| A. Bewley, Z. Ge, L. Ott, F. Ramos, B. Upcroft. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), 2016. | ||||||||||||||
| ITM 3. |
50.6 ±22.3 |
48.6 ±15.6 | 78.6 | 397 (32.0) | 320 (25.8) | 19,495 | 233,940 | 54.8 | 93.6 | 4.4 | 2,431 (44.4) | 4,533 (82.7) | 1.8 | |
| Pivot Correlation Network with Individualized Tubelets for Efficient Multi-object Tracking | ||||||||||||||
| LPC_MOT 4. |
56.3 ±18.9 |
62.5 ±14.9 | 79.7 | 424 (34.1) | 313 (25.2) | 11,726 | 213,056 | 58.8 | 96.3 | 2.6 | 1,562 (26.6) | 1,865 (31.7) | 0.7 | |
| Tsinghua University & AIBEE Research. | ||||||||||||||
| HOMI_Tracker 5. |
51.2 ±22.6 |
43.0 ±13.3 | 79.6 | 423 (34.1) | 312 (25.1) | 16,094 | 232,259 | 55.1 | 94.7 | 3.6 | 3,937 (71.4) | 6,458 (117.2) | 7.5 | |
| UnsupTrack 6. |
53.6 ±19.0 |
50.6 ±13.3 | 80.1 | 376 (30.3) | 311 (25.0) | 6,439 | 231,298 | 55.3 | 97.8 | 1.4 | 2,178 (39.4) | 4,335 (78.4) | 1.3 | |
| S. Karthik, A. Prabhu, V. Gandhi. Simple Unsupervised Multi-Object Tracking. In Arxiv, 2020. | ||||||||||||||
| BBT 7. |
46.8 ±21.3 |
42.2 ±12.0 | 78.0 | 312 (25.1) | 289 (23.3) | 35,014 | 236,176 | 54.4 | 88.9 | 7.8 | 3,880 (71.4) | 7,207 (132.6) | 8.0 | |
| MTAP-D-20-01870 Tracking Subjects and Detecting Relationships in Crowded City Videos (under review) | ||||||||||||||
| MPNTrack 8. |
57.6 ±19.7 |
59.1 ±14.5 | 79.0 | 474 (38.2) | 279 (22.5) | 16,953 | 201,384 | 61.1 | 94.9 | 3.8 | 1,210 (19.8) | 1,420 (23.2) | 6.5 | |
| G. Brasó, L. Leal-Taixé. Learning a Neural Solver for Multiple Object Tracking. In CVPR, 2020. | ||||||||||||||
| MLT 9. |
48.9 ±21.9 |
54.6 ±10.4 | 78.0 | 384 (30.9) | 274 (22.1) | 45,660 | 216,803 | 58.1 | 86.8 | 10.2 | 2,187 (37.6) | 3,067 (52.8) | 3.7 | |
| 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. | ||||||||||||||
| GMPHD_Rd20 10. |
44.7 ±20.7 |
43.5 ±9.7 | 77.5 | 293 (23.6) | 274 (22.1) | 42,778 | 236,116 | 54.4 | 86.8 | 9.6 | 7,492 (137.8) | 11,153 (205.1) | 25.2 | |
| Tracker | MOTA | IDF1 | MOTP | MT | ML | FP | FN | Recall | Precision | FAF | ID Sw. | Frag | Hz | |
| SFS 11. |
50.8 ±17.4 |
41.1 ±9.6 | 74.9 | 341 (27.5) | 251 (20.2) | 50,139 | 200,932 | 61.2 | 86.3 | 11.2 | 3,503 (57.3) | 7,617 (124.5) | 0.1 | |
| MTSFS： Online Multi-Object Tracking Based on Salient Feature Selection in Crowded Scenes | ||||||||||||||
| MOT20_TBC 12. |
54.5 ±17.2 |
50.1 ±11.5 | 77.3 | 415 (33.4) | 245 (19.7) | 37,937 | 195,242 | 62.3 | 89.5 | 8.5 | 2,449 (39.3) | 2,580 (41.4) | 5.6 | |
| TrTEST 13. |
57.0 ±20.4 |
48.8 ±13.3 | 79.7 | 499 (40.2) | 243 (19.6) | 26,770 | 190,486 | 63.2 | 92.4 | 6.0 | 5,271 (83.4) | 4,877 (77.2) | 1.0 | |
| Surveily 14. |
44.6 ±24.3 |
42.5 ±12.2 | 76.1 | 393 (31.6) | 236 (19.0) | 71,208 | 211,064 | 59.2 | 81.1 | 15.9 | 4,334 (73.2) | 6,646 (112.2) | 29.8 | |
| ALBOD 15. |
56.5 ±20.8 |
51.1 ±10.4 | 79.4 | 506 (40.7) | 228 (18.4) | 36,864 | 184,582 | 64.3 | 90.0 | 8.2 | 3,727 (57.9) | 4,308 (67.0) | 1.2 | |
| Anonymous submission | ||||||||||||||
| FGRNetIV 16. |
55.4 ±17.6 |
52.7 ±10.8 | 79.4 | 508 (40.9) | 221 (17.8) | 38,973 | 189,715 | 63.3 | 89.4 | 8.7 | 2,159 (34.1) | 3,839 (60.6) | 1.3 | |
| center_reid 17. |
56.6 ±18.3 |
65.0 ±11.4 | 77.0 | 668 (53.8) | 141 (11.4) | 98,181 | 121,688 | 76.5 | 80.1 | 21.9 | 4,643 (60.7) | 9,188 (120.1) | 7.0 | |
| Sequences | Frames | Trajectories | Boxes |
| 4 | 4479 | 1501 | 765465 |
Difficulty Analysis
Sequence difficulty (from easiest to hardest, measured by average MOTA)
MOT20-04
(71.0 MOTA)
MOT20-07
(53.7 MOTA)
MOT20-06
(29.9 MOTA)
MOT20-08
(20.3 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 |
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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. |
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This method used the provided detection set as input. |
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This method used a private detection set as input. |
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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. |
