MOT17Det Results
Click on a measure to sort the table accordingly. See below for a more detailed description.
| Tracker | AP | MODA | MODP | FAF | TP | FP | FN | Recall | Precision | F1 | Hz |
| SeedDet 1. |
0.90 ±0.07 |
81.8 ±12.1 | 82.8 | 2.3 | 107,291 | 13,631 | 7,273 | 93.7 | 88.7 | 91.1 | 8.2 |
| seedland multi-target detection | |||||||||||
| MixNet 2. |
0.90 ±0.07 |
74.3 ±15.0 | 78.6 | 3.8 | 107,764 | 22,661 | 6,800 | 94.1 | 82.6 | 88.0 | 197.3 |
| AInnoDetV2 3. |
0.89 ±0.05 |
-58.4 ±126.7 | 79.1 | 29.5 | 107,733 | 174,608 | 6,831 | 94.0 | 38.2 | 54.3 | 296.0 |
| AInnovation: PC Attention Net | |||||||||||
| PA_Det_NJ 4. |
0.89 ±0.06 |
82.1 ±20.1 | 79.3 | 2.4 | 108,480 | 14,417 | 6,084 | 94.7 | 88.3 | 91.4 | 59.2 |
| PA_TECH_NJ | |||||||||||
| YTLAB 5. |
0.89 ±0.07 |
76.7 ±13.1 | 80.2 | 2.8 | 104,555 | 16,685 | 10,009 | 91.3 | 86.2 | 88.7 | 22.3 |
| Z. Cai, Q. Fan, R. Feris, N. Vasconcelos. A unified multi-scale deep convolutional neural network for fast object detection. In European Conference on Computer Vision, 2016. | |||||||||||
| KDNT 6. |
0.89 ±0.07 |
67.1 ±22.4 | 80.1 | 4.8 | 105,473 | 28,623 | 9,091 | 92.1 | 78.7 | 84.8 | 0.8 |
| F. Yu, W. Li, Q. Li, Y. Liu, X. Shi, J. Yan. POI: Multiple Object Tracking with High Performance Detection and Appearance Feature. In BMTT, SenseTime Group Limited, 2016. | |||||||||||
| F_ViPeD_B 7. |
0.89 ±0.06 |
-14.4 ±115.1 | 77.4 | 20.8 | 106,698 | 123,194 | 7,831 | 93.2 | 46.4 | 62.0 | 14.8 |
| L. Ciampi, N. Messina, F. Falchi, C. Gennaro, G. Amato. Virtual to Real Adaptation of Pedestrian Detectors. In Sensors, 2020. | |||||||||||
| GNN_SDT 8. |
0.89 ±0.09 |
78.1 ±8.3 | 81.3 | 2.4 | 103,895 | 14,397 | 10,669 | 90.7 | 87.8 | 89.2 | 5,919.0 |
| Y. Wang, X. Weng, K. Kitani. Joint Detection and Multi-Object Tracking with Graph Neural Networks. In arXiv, 2020. | |||||||||||
| ISE_Detv2 9. |
0.88 ±0.05 |
67.4 ±12.8 | 79.9 | 4.9 | 106,094 | 28,865 | 8,470 | 92.6 | 78.6 | 85.0 | 3.2 |
| MIFD | |||||||||||
| ZIZOM 10. |
0.81 ±0.05 |
72.0 ±22.0 | 79.8 | 2.2 | 95,414 | 12,990 | 19,139 | 83.3 | 88.0 | 85.6 | 2.4 |
| C. Lin, L. Jiwen, G. Wang, J. Zhou. Graininess-Aware Deep Feature Learning for Pedestrian Detection. In ECCV, 2018. | |||||||||||
| Tracker | AP | MODA | MODP | FAF | TP | FP | FN | Recall | Precision | F1 | Hz |
| SDP 11. |
0.81 ±0.12 |
76.9 ±16.2 | 78.0 | 1.3 | 95,699 | 7,599 | 18,865 | 83.5 | 92.6 | 87.9 | 0.6 |
| F. Yang, W. Choi, Y. Lin. Exploit All the Layers: Fast and Accurate CNN Object Detector With Scale Dependent Pooling and Cascaded Rejection Classifiers. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. | |||||||||||
| FRCNN 12. |
0.72 ±0.13 |
68.5 ±0.0 | 78.0 | 1.7 | 88,601 | 10,081 | 25,963 | 77.3 | 89.8 | 83.1 | 5.1 |
| S. Ren, K. He, R. Girshick, J. Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In NIPS, 2015. | |||||||||||
| DPM 13. |
0.61 ±0.14 |
31.2 ±10.8 | 75.8 | 7.1 | 78,007 | 42,308 | 36,557 | 68.1 | 64.8 | 66.4 | 19.7 |
| P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. In TPAMI, 2010. | |||||||||||
| MHD 14. |
0.49 ±0.20 |
11.2 ±42.9 | 69.9 | 8.8 | 64,637 | 51,801 | 49,927 | 56.4 | 55.5 | 56.0 | 3.0 |
| Mobilenet-based Human Detection | |||||||||||
| YLHDv2 15. |
0.46 ±0.11 |
56.9 ±12.5 | 73.2 | 2.5 | 80,093 | 14,938 | 34,471 | 69.9 | 84.3 | 76.4 | 11.8 |
| https://arxiv.org/abs/1612.08242 | |||||||||||
| HDGP 16. |
0.45 ±0.20 |
42.1 ±20.1 | 76.4 | 1.3 | 55,680 | 7,436 | 58,884 | 48.6 | 88.2 | 62.7 | 0.6 |
| A. Garcia-Martin, R. Sanchez-Matilla, J. Martinez. Hierarchical detection of persons in groups. In Signal, Image and Video Processing, 2017. | |||||||||||
| VDet 17. |
0.44 ±0.19 |
44.7 ±19.3 | 75.7 | 1.0 | 56,980 | 5,765 | 57,584 | 49.7 | 90.8 | 64.3 | 5.9 |
| Vitrociset Detection Algorithm | |||||||||||
| ACF 18. |
0.32 ±0.00 |
18.1 ±0.0 | 72.1 | 2.8 | 37,312 | 16,539 | 77,252 | 32.6 | 69.3 | 44.3 | 74.0 |
| P. Dollar, R. Appel, S. Belongie, P. Perona. Fast Feature Pyramids for Object Detection. In TPAMI, 2014. | |||||||||||
| SSDMNv3EDD3 19. |
0.22 ±0.16 |
-43.7 ±34.4 | 68.0 | 14.9 | 38,288 | 88,370 | 76,276 | 33.4 | 30.2 | 31.7 | 4.2 |
| Sequences | Frames | Trajectories | Boxes |
| 7 | 5919 | 785 | 188076 |
Difficulty Analysis
Sequence difficulty (from easiest to hardest, measured by average AP)
MOT17-08
(0.82 AP)
MOT17-03
(0.77 AP)
MOT17-06
(0.73 AP)
...
...
MOT17-01
(0.62 AP)
MOT17-14
(0.52 AP)
Evaluation Measures
Lower is better. Higher is better.| Measure | Better | Perfect | Description |
| AP | higher | 1 | Average Precision taken over a set of reference recall values (0:0.1:1) |
| MODA | higher | 100% | Multi-Object Detection Accuracy [1]. This measure combines false positives and missed targets. |
| MODP | higher | 100% | Multi-Object Detection Precision [1]. The misalignment between the annotated and the predicted bounding boxes. |
| FAF | lower | 0 | The average number of false alarms per frame. |
| TP | higher | #GT | The total number of true positives. |
| 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). |
| F1 | higher | 100% | Harmonic mean of precision and recall. |
| 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. |
