MOT17Det Results

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


DetectorAPMODAMODPFAFTPFP FNPrecisionRecall
PA_MOT_Det
1.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
PA_Det_NJ
2.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
UNV_Det
3.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
meow_det2
4.
0.87
-105.678.138.4106,378227,3898,18631.992.9
Anonymous submission
meow_det
5.
0.88
-41.478.425.9105,644153,1308,92040.892.2
Anonymous submission
HRCN
6.
0.87
-53.678.528.2105,551166,9689,01338.792.1
Anonymous submission
KDNT
7.
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.
HRCNdet
8.
0.86
-39.278.625.3104,927149,8849,63741.291.6
Anonymous submission
YTLAB
9.
0.89
76.780.22.8104,55516,68510,00986.291.3
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.
XYZvX
10.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
DetectorAPMODAMODPFAFTPFP FNPrecisionRecall
HBv20Rep
11.
0.80
69.178.13.297,94818,83016,61683.985.5
Anonymous submission
hbv20
12.
0.80
70.578.52.897,00016,28517,56485.684.7
Anonymous submission
ViPeD
13.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
RTFCP
14.
0.76
-64.476.528.896,504170,27518,06036.284.2
Anonymous submission
SDP
15.
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.
HumanBoxes
16.
0.79
58.077.34.995,65829,18618,90676.683.5
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.
XYZvN
18.
0.80
69.378.52.594,44115,03720,12386.382.4
Anonymous submission
Strong_SSD
19.
0.72
41.577.17.491,08043,57623,48467.679.5
Anonymous submission
Cas_CH_17
20.
0.65
51.580.05.189,43630,40425,11774.678.1
Anonymous submission
DetectorAPMODAMODPFAFTPFP FNPrecisionRecall
FRCNN
21.
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.
YOLO_Virt
22.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
yolov3_ft
23.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
YLHDv2
24.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
DPM
25.
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.
YOLOv3_XYZ
26.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
VIS_DET1
27.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
MHD
28.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
VDet
29.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
muDetect
30. new
0.43
42.076.81.355,9527,83458,61287.748.8
Anonymous submission
DetectorAPMODAMODPFAFTPFP FNPrecisionRecall
HDGP
31.
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
32.
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.
XYZv3
33.
0.00
-67.278.311.58768,309101,3650.10.1
Anonymous submission
cn
34.
0.00
-97.062.518.9891112,039113,6730.80.8
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.