MOT17Det Results

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


DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
YTLAB
1.
0.89
76.780.22.8104,55516,68510,00986.291.3
Anonymous submission
KDNT
2.
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.
MKDNT
3.
0.89
75.980.32.7103,14316,18511,42186.490.0
Anonymous submission
SDP
4.
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.
MSDP
5.
0.81
77.478.10.893,7385,02420,82694.981.8
Anonymous submission
SEN
6.
0.80
65.078.13.595,23020,71419,33482.183.1
Anonymous submission
FRCNN
7.
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
8.
0.71
69.278.41.186,0866,77428,47892.775.1
Anonymous submission
Dector1
9.
0.71
68.478.41.184,8646,45929,70092.974.1
Anonymous submission
ICO
10.
0.71
56.177.22.780,39516,15334,16983.370.2
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
uvdetector
11.
0.68
43.675.35.884,34834,37630,21671.073.6
Uncanny Vision, Bangalore, India
MDPM
12.
0.61
43.576.04.274,54624,65940,01875.165.1
Anonymous submission
DPM
13.
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.
DectFrCNN
14.
0.59
-2.973.913.174,50377,78540,06148.965.0
Anonymous submission
PD_MASK
15.
0.57
33.076.09.392,87155,12021,69362.881.1
Anonymous submission
FRCNNRS101
16.
0.52
47.175.51.864,94710,93149,61785.656.7
Anonymous submission
YLHDv2
17.
0.46
56.973.22.580,09314,93834,47184.369.9
Anonymous submission
HDGP
18.
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.
ACF
19.
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.

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.