MOT17Det Results

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


DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
ACF
1.
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.
Cas_CH_17
2.
0.65
51.580.05.189,43630,40425,11774.678.1
Anonymous submission
cn
3.
0.00
-97.062.518.9891112,039113,6730.80.8
Anonymous submission
DPM
4.
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.
FRCNN
5.
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.
hbv20
6.
0.80
70.578.52.897,00016,28517,56485.684.7
Anonymous submission
HBv20Rep
7.
0.80
69.178.13.297,94818,83016,61683.985.5
Anonymous submission
HDGP
8.
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.
HRCN
9.
0.87
-53.678.528.2105,551166,9689,01338.792.1
Anonymous submission
HRCNdet
10.
0.86
-39.278.625.3104,927149,8849,63741.291.6
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
HumanBoxes
11.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
KDNT
12.
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.
meow_det
13.
0.88
-41.478.425.9105,644153,1308,92040.892.2
Anonymous submission
meow_det2
14.
0.87
-105.678.138.4106,378227,3898,18631.992.9
Anonymous submission
MHD
15.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
muDetect
16. new
0.43
42.076.81.355,9527,83458,61287.748.8
Anonymous submission
PA_Det_NJ
17.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
PA_MOT_Det
18.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
RTFCP
19.
0.76
-64.476.528.896,504170,27518,06036.284.2
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.
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
Strong_SSD
21.
0.72
41.577.17.491,08043,57623,48467.679.5
Anonymous submission
UNV_Det
22.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
VDet
23.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
ViPeD
24.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
VIS_DET1
25.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
XYZv3
26.
0.00
-67.278.311.58768,309101,3650.10.1
Anonymous submission
XYZvN
27.
0.80
69.378.52.594,44115,03720,12386.382.4
Anonymous submission
XYZvX
28.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
YLHDv2
29.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
yolov3_ft
30.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
YOLOv3_XYZ
31.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
YOLO_Virt
32.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
YTLAB
33.
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.
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.

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.