MOT17Det Results

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


DetectorAPMODA MODPFAFTPFPFNPrecisionRecall
cn
1.
0.00
-97.062.518.9891112,039113,6730.80.8
Anonymous submission
MHD
2.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
YOLO_Virt
3.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
yolov3_ft
4.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
ACF
5.
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.
YOLOv3_XYZ
6.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
YLHDv2
7.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
ViPeD
8.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
motrcnn
9.
0.64
54.975.23.282,08419,23432,48081.071.6
Anonymous submission
VDet
10.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
DetectorAPMODA MODPFAFTPFPFNPrecisionRecall
DPM
11.
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.
muDetectV3
12.
0.34
27.475.94.658,50327,13756,06168.351.1
Anonymous submission
HDGP
13.
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.
RTFCP
14.
0.76
-64.476.528.896,504170,27518,06036.284.2
Anonymous submission
muDetectV2
15.
0.39
38.476.82.558,66414,63655,90080.051.2
Anonymous submission
muDetect
16.
0.43
42.076.81.355,9527,83458,61287.748.8
Anonymous submission
Strong_SSD
17.
0.72
53.477.04.990,03628,85424,52875.778.6
Anonymous submission
HumanBoxes
18.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
SDP
19.
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.
FRCNN
20.
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.
DetectorAPMODA MODPFAFTPFPFNPrecisionRecall
HBv20Rep
21.
0.80
69.178.13.297,94818,83016,61683.985.5
Anonymous submission
meow_det2
22.
0.87
-105.678.138.4106,378227,3898,18631.992.9
Anonymous submission
XYZv3
23.
0.00
-67.278.311.58768,309101,3650.10.1
Anonymous submission
meow_det
24.
0.88
-41.478.425.9105,644153,1308,92040.892.2
Anonymous submission
hbv20
25.
0.80
70.578.52.897,00016,28517,56485.684.7
Anonymous submission
XYZvN
26.
0.80
69.378.52.594,44115,03720,12386.382.4
Anonymous submission
HRCN
27.
0.87
-53.678.528.2105,551166,9689,01338.792.1
Anonymous submission
HRCNdet
28.
0.86
-39.278.625.3104,927149,8849,63741.291.6
Anonymous submission
UNV_Det
29.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
PA_MOT_Det
30.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
DetectorAPMODA MODPFAFTPFPFNPrecisionRecall
PA_Det_NJ
31.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
XYZvX
32.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
VIS_DET1
33.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
ZIZOM
34.
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.
Cas_CH_17
35.
0.65
51.580.05.189,43630,40425,11774.678.1
Anonymous submission
KDNT
36.
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.
YTLAB
37.
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.

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.