MOT17Det Results
Click on a measure to sort the table accordingly. See below for a more detailed description.
| Detector | AP | MODA | MODP | FAF | TP | FP | FN | Precision | Recall |
| UNV_Det 1. | 0.89 | 81.9 | 79.0 | 2.1 | 106,478 | 12,704 | 8,086 | 89.3 | 92.9 |
| Anonymous submission | |||||||||
| PA_Det_NJ 2. | 0.89 | 82.1 | 79.3 | 2.4 | 108,480 | 14,417 | 6,084 | 88.3 | 94.7 |
| PA_TECH_NJ | |||||||||
| YTLAB 3. | 0.89 | 76.7 | 80.2 | 2.8 | 104,555 | 16,685 | 10,009 | 86.2 | 91.3 |
| 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 4. | 0.89 | 67.1 | 80.1 | 4.8 | 105,473 | 28,623 | 9,091 | 78.7 | 92.1 |
| 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. | |||||||||
| PA_MOT_Det 5. | 0.86 | 83.4 | 79.2 | 2.2 | 108,581 | 13,071 | 5,983 | 89.3 | 94.8 |
| Anonymous submission | |||||||||
| ZIZOM 6. | 0.81 | 72.0 | 79.8 | 2.2 | 95,414 | 12,990 | 19,139 | 88.0 | 83.3 |
| C. Lin, L. Jiwen, G. Wang, J. Zhou. Graininess-Aware Deep Feature Learning for Pedestrian Detection. In ECCV, 2018. | |||||||||
| SDP 7. | 0.81 | 76.9 | 78.0 | 1.3 | 95,699 | 7,599 | 18,865 | 92.6 | 83.5 |
| 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. | |||||||||
| hbv20 8. | 0.80 | 70.5 | 78.5 | 2.8 | 97,000 | 16,285 | 17,564 | 85.6 | 84.7 |
| Anonymous submission | |||||||||
| HBv20Rep 9. | 0.80 | 69.1 | 78.1 | 3.2 | 97,948 | 18,830 | 16,616 | 83.9 | 85.5 |
| Anonymous submission | |||||||||
| ViPeD 10. | 0.80 | 75.4 | 74.1 | 1.8 | 96,884 | 10,489 | 17,680 | 90.2 | 84.6 |
| Anonymous submission | |||||||||
| Detector | AP | MODA | MODP | FAF | TP | FP | FN | Precision | Recall |
| HumanBoxes 11. | 0.79 | 58.0 | 77.3 | 4.9 | 95,658 | 29,186 | 18,906 | 76.6 | 83.5 |
| Anonymous submission | |||||||||
| FRCNN 12. | 0.72 | 68.5 | 78.0 | 1.7 | 88,601 | 10,081 | 25,963 | 89.8 | 77.3 |
| S. Ren, K. He, R. Girshick, J. Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In NIPS, 2015. | |||||||||
| YOLO_Virt 13. | 0.71 | 63.9 | 70.5 | 2.2 | 86,429 | 13,254 | 28,135 | 86.7 | 75.4 |
| Anonymous submission | |||||||||
| yolov3_ft 14. | 0.70 | 60.2 | 71.8 | 2.3 | 82,767 | 13,809 | 31,797 | 85.7 | 72.2 |
| Anonymous submission | |||||||||
| yolo_JTA 15. | 0.62 | 54.3 | 71.5 | 2.3 | 75,845 | 13,658 | 38,719 | 84.7 | 66.2 |
| Anonymous submission | |||||||||
| DPM 16. | 0.61 | 31.2 | 75.8 | 7.1 | 78,007 | 42,308 | 36,557 | 64.8 | 68.1 |
| P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. In TPAMI, 2010. | |||||||||
| YOLOv3_XYZ 17. | 0.60 | 37.5 | 73.1 | 5.8 | 77,182 | 34,267 | 37,382 | 69.3 | 67.4 |
| Anonymous submission | |||||||||
| VIS_DET1 18. | 0.54 | 58.6 | 79.8 | 0.6 | 70,618 | 3,516 | 43,935 | 95.3 | 61.6 |
| Anonymous submission | |||||||||
| v3 19. | 0.52 | 41.0 | 77.0 | 1.8 | 57,554 | 10,551 | 57,010 | 84.5 | 50.2 |
| Anonymous submission | |||||||||
| MHD 20. | 0.49 | 11.2 | 69.9 | 8.8 | 64,637 | 51,801 | 49,927 | 55.5 | 56.4 |
| Mobilenet-based Human Detection | |||||||||
| Detector | AP | MODA | MODP | FAF | TP | FP | FN | Precision | Recall |
| YLHDv2 21. | 0.46 | 56.9 | 73.2 | 2.5 | 80,093 | 14,938 | 34,471 | 84.3 | 69.9 |
| https://arxiv.org/abs/1612.08242 | |||||||||
| HDGP 22. | 0.45 | 42.1 | 76.4 | 1.3 | 55,680 | 7,436 | 58,884 | 88.2 | 48.6 |
| A. Garcia-Martin, R. Sanchez-Matilla, J. Martinez. Hierarchical detection of persons in groups. In Signal, Image and Video Processing, 2017. | |||||||||
| VDet 23. | 0.44 | 44.7 | 75.7 | 1.0 | 56,980 | 5,765 | 57,584 | 90.8 | 49.7 |
| Vitrociset Detection Algorithm | |||||||||
| ACF 24. | 0.32 | 18.1 | 72.1 | 2.8 | 37,312 | 16,539 | 77,252 | 69.3 | 32.6 |
| P. Dollar, R. Appel, S. Belongie, P. Perona. Fast Feature Pyramids for Object Detection. In TPAMI, 2014. | |||||||||
| cn 25. | 0.00 | -97.0 | 62.5 | 18.9 | 891 | 112,039 | 113,673 | 0.8 | 0.8 |
| Anonymous submission | |||||||||
| XYZv3 26. | 0.00 | -67.2 | 78.3 | 11.5 | 87 | 68,309 | 101,365 | 0.1 | 0.1 |
| Anonymous submission | |||||||||
Benchmark Statistics
| Sequences | Frames | Boxes |
| 7 | 5919 | 188076 |
Evaluation Measures
Lower is better. Higher is better.| Measure | Better | Perfect | Description |
| AP | higher | 100 % | Average Precision taken over a set of reference recall values (0:0.1:1) |
| MODA | higher | 100 % | Multiple Object Detection Accuracy [1]. This measure combines false positives and missed targets. |
| MOTP | higher | 100 % | Multiple 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). |
| Precision | higher | 100 % | Ratio of TP / (TP+FP). |
| Recall | higher | 100 % | Ratio of correct detections to total number of GT boxes. |
Legend
| Symbol | Description |
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This entry has been submitted or updated less than a week ago. |
