MOT17Det Results

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


DetectorAP MODAMODPFAFTPFPFNPrecisionRecall
DectFrCNN
1.
0.59
-2.973.913.174,50377,78540,06148.965.0
Anonymous submission
ACF
2.
0.32
18.172.12.837,31216,53977,25269.332.6
P. Dollar, R. Appel, S. Belongie, P. Perona. Fast Feature Pyramids for Object Detection. In TPAMI, 2014.
DPM
3.
0.61
31.275.87.178,00742,30836,55764.868.1
P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan. Object Detection with Discriminatively Trained Part Based Models. In TPAMI, 2010.
PD_MASK
4.
0.57
33.076.09.392,87155,12021,69362.881.1
Anonymous submission
HDGP
5.
0.45
42.176.41.355,6807,43658,88488.248.6
A. Garcia-Martin, R. Sanchez-Matilla, J. Martinez. Hierarchical detection of persons in groups. In Signal, Image and Video Processing, 2017.
MDPM
6.
0.61
43.576.04.274,54624,65940,01875.165.1
Anonymous submission
uvdetector
7.
0.68
43.675.35.884,34834,37630,21671.073.6
Uncanny Vision, Bangalore, India
FRCNNRS101
8.
0.52
47.175.51.864,94710,93149,61785.656.7
Anonymous submission
ICO
9.
0.71
56.177.22.780,39516,15334,16983.370.2
Anonymous submission
YLHDv2
10.
0.46
56.973.22.580,09314,93834,47184.369.9
Anonymous submission
DetectorAP MODAMODPFAFTPFPFNPrecisionRecall
yolov3
11.
0.77
62.974.73.794,14622,04720,41881.082.2
Anonymous submission
SEN
12.
0.80
65.078.13.595,23020,71419,33482.183.1
Anonymous submission
KDNT
13.
0.89
67.180.14.8105,47328,6239,09178.792.1
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.
Dector1
14.
0.71
68.478.41.184,8646,45929,70092.974.1
Anonymous submission
FRCNN
15.
0.72
68.578.01.788,60110,08125,96389.877.3
S. Ren, K. He, R. Girshick, J. Sun. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. In NIPS, 2015.
MFRCNN
16.
0.71
69.278.41.186,0866,77428,47892.775.1
Anonymous submission
ZIZOM
17.
0.81
72.079.82.295,41412,99019,13988.083.3
C. Lin, L. Jiwen, G. Wang, J. Zhou. Graininess-Aware Deep Feature Learning for Pedestrian Detection. In ECCV, 2018.
MKDNT
18.
0.89
75.980.32.7103,14316,18511,42186.490.0
Anonymous submission
YTLAB
19.
0.89
76.780.22.8104,55516,68510,00986.291.3
Anonymous submission
SDP
20.
0.81
76.978.01.395,6997,59918,86592.683.5
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.
DetectorAP MODAMODPFAFTPFPFNPrecisionRecall
MSDP
21.
0.81
77.478.10.893,7385,02420,82694.981.8
Anonymous submission

Benchmark Statistics

SequencesFramesBoxes
75919188076

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
new This entry has been submitted or updated less than a week ago.

References:


[1] Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J.S., Mostefa, D. & Soundararajan, P. The CLEAR 2006 Evaluation. In CLEAR, 2006.