MOT17Det Results

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


DetectorAP MODAMODPFAFTPFPFNPrecisionRecall
meow_det2
1.
0.87
-105.678.138.4106,378227,3898,18631.992.9
Anonymous submission
cn
2.
0.00
-97.062.518.9891112,039113,6730.80.8
Anonymous submission
XYZv3
3.
0.00
-67.278.311.58768,309101,3650.10.1
Anonymous submission
RTFCP
4.
0.76
-64.476.528.896,504170,27518,06036.284.2
Anonymous submission
HRCN
5.
0.87
-53.678.528.2105,551166,9689,01338.792.1
Anonymous submission
meow_det
6.
0.88
-41.478.425.9105,644153,1308,92040.892.2
Anonymous submission
HRCNdet
7.
0.86
-39.278.625.3104,927149,8849,63741.291.6
Anonymous submission
MHD
8.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
ACF
9.
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.
DPM
10.
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.
DetectorAP MODAMODPFAFTPFPFNPrecisionRecall
YOLOv3_XYZ
11.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
Strong_SSD
12.
0.72
41.577.17.491,08043,57623,48467.679.5
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.
VDet
14.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
Cas_CH_17
15.
0.65
51.580.05.189,43630,40425,11774.678.1
Anonymous submission
YLHDv2
16.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
HumanBoxes
17.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
VIS_DET1
18.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
yolov3_ft
19.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
YOLO_Virt
20.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
DetectorAP MODAMODPFAFTPFPFNPrecisionRecall
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.
FRCNN
22.
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.
HBv20Rep
23.
0.80
69.178.13.297,94818,83016,61683.985.5
Anonymous submission
XYZvN
24.
0.80
69.378.52.594,44115,03720,12386.382.4
Anonymous submission
hbv20
25.
0.80
70.578.52.897,00016,28517,56485.684.7
Anonymous submission
ZIZOM
26.
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.
XYZvX
27.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
ViPeD
28.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
YTLAB
29.
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.
SDP
30.
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.
DetectorAP MODAMODPFAFTPFPFNPrecisionRecall
UNV_Det
31.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
PA_Det_NJ
32.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
PA_MOT_Det
33.
0.86
83.479.22.2108,58113,0715,98389.394.8
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.