MOT17Det Results

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


DetectorAPMODAMODPFAFTP FPFNPrecisionRecall
VIS_DET1
1.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
VDet
2.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
HDGP
3.
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.
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.
muDetect
5. new
0.43
42.076.81.355,9527,83458,61287.748.8
Anonymous submission
FRCNN
6.
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.
ViPeD
7.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
UNV_Det
8.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
ZIZOM
9.
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.
PA_MOT_Det
10.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
DetectorAPMODAMODPFAFTP FPFNPrecisionRecall
YOLO_Virt
11.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
yolov3_ft
12.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
PA_Det_NJ
13.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
YLHDv2
14.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
XYZvN
15.
0.80
69.378.52.594,44115,03720,12386.382.4
Anonymous submission
XYZvX
16.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
hbv20
17.
0.80
70.578.52.897,00016,28517,56485.684.7
Anonymous submission
ACF
18.
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.
YTLAB
19.
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.
HBv20Rep
20.
0.80
69.178.13.297,94818,83016,61683.985.5
Anonymous submission
DetectorAPMODAMODPFAFTP FPFNPrecisionRecall
KDNT
21.
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.
HumanBoxes
22.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
Cas_CH_17
23.
0.65
51.580.05.189,43630,40425,11774.678.1
Anonymous submission
YOLOv3_XYZ
24.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
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.
Strong_SSD
26.
0.72
41.577.17.491,08043,57623,48467.679.5
Anonymous submission
MHD
27.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
XYZv3
28.
0.00
-67.278.311.58768,309101,3650.10.1
Anonymous submission
cn
29.
0.00
-97.062.518.9891112,039113,6730.80.8
Anonymous submission
HRCNdet
30.
0.86
-39.278.625.3104,927149,8849,63741.291.6
Anonymous submission
DetectorAPMODAMODPFAFTP FPFNPrecisionRecall
meow_det
31.
0.88
-41.478.425.9105,644153,1308,92040.892.2
Anonymous submission
HRCN
32.
0.87
-53.678.528.2105,551166,9689,01338.792.1
Anonymous submission
RTFCP
33.
0.76
-64.476.528.896,504170,27518,06036.284.2
Anonymous submission
meow_det2
34.
0.87
-105.678.138.4106,378227,3898,18631.992.9
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.