MOT17Det Results

Download all results for this benchmark

Click on a measure to sort the table accordingly. See below for a more detailed description.


Benchmark Statistics

TrackerAPMODAMODPFAFTPFPFNRcllPrcnF1Hz
SGT_det
1. 		0.90 86.6 82.4 1.3 106,849 7,688 7,715 93.3 93.3 93.3 20.8
J. Hyun, M. Kang, D. Wee, D. Yeung. Detection recovery in online multi-object tracking with sparse graph tracker. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023.
SeedDet
2. 		0.90 81.8 82.8 2.3 107,291 13,631 7,273 93.7 88.7 91.1 8.2
seedland multi-target detection
SW_YoloX_det
3. 		0.90 86.4 83.0 1.0 105,082 6,082 9,482 91.7 94.5 93.1 12.6
C.-Y. Tsai, R.-Y. Wang and Y.-C. Chiu, “SW-YOLOX: A Real-Time Pedestrian Detection System Based on Sliding Window-Mixed Attention Mechanism in YOLOX Lightweight Network,” Neurocomputing, under review
AInnoDetV2
4. 		0.89 -58.4 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
5. 		0.89 82.1 79.3 2.4 108,480 14,417 6,084 94.7 88.3 91.4 59.2
PA_TECH_NJ
YTLAB
6. 		0.89 76.7 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
7. 		0.89 67.1 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
8. 		0.89 -14.4 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
9. 		0.89 78.1 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
10. 		0.88 67.4 79.9 4.9 106,094 28,865 8,470 92.6 78.6 85.0 3.2
MIFD
TrackerAPMODAMODPFAFTPFPFNRcllPrcnF1Hz
SW_YoloX_N
11. 		0.82 82.5 82.2 1.3 102,022 7,502 12,542 89.1 93.2 91.1 46.4
C.-Y. Tsai, R.-Y. Wang and Y.-C. Chiu, “SW-YOLOX: A Real-Time Pedestrian Detection System Based on Sliding Window-Mixed Attention Mechanism in YOLOX Lightweight Network,” Neurocomputing, under review.
ZIZOM
12. 		0.81 72.0 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.
SDP
13. 		0.81 76.9 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
14. 		0.72 68.5 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
15. 		0.61 31.2 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
16. 		0.49 11.2 69.9 8.8 64,637 51,801 49,927 56.4 55.5 56.0 3.0
Mobilenet-based Human Detection
YLHDv2
17. 		0.46 56.9 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
18. 		0.45 42.1 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
19. 		0.44 44.7 75.7 1.0 56,980 5,765 57,584 49.7 90.8 64.3 5.9
Vitrociset Detection Algorithm
ACF
20. 		0.32 18.1 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.
SequencesFramesTrajectoriesBoxes
75919785188076

Evaluation Measures

Lower is better. Higher is better.
Measure Better Perfect Description
AP higher 1Average 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 0The average number of false alarms per frame.
TP higher #GTThe total number of true positives.
FP lower 0The total number of false positives.
FN lower 0The total number of false negatives (missed targets).
Rcll higher 100%Ratio of correct detections to total number of GT boxes.
Prcn 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
online method 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.
using public detections This method used the provided detection set as input.
using private detections This method used a private detection set as input.
new This entry has been submitted or updated less than a week ago.

References:


[1] Bernardin, K. & Stiefelhagen, R. Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. Image and Video Processing, 2008(1):1-10, 2008.



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