MOT17Det Results

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


Detector APMODAMODPFAFTPFPFNPrecisionRecall
cn
1.
0.00
-97.062.518.9891112,039113,6730.80.8
Anonymous submission
XYZv3
2.
0.00
-67.278.311.58768,309101,3650.10.1
Anonymous submission
ACF
3.
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.
VDet
4.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
HDGP
5.
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.
YLHDv2
6.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
MHD
7.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
VIS_DET1
8.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
YOLOv3_XYZ
9.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
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.
Detector APMODAMODPFAFTPFPFNPrecisionRecall
Cas_CH_17
11.
0.65
51.580.05.189,43630,40425,11774.678.1
Anonymous submission
yolov3_ft
12.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
YOLO_Virt
13.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
FRCNN
14.
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.
Strong_SSD
15.
0.72
41.577.17.491,08043,57623,48467.679.5
Anonymous submission
RTFCP
16.
0.76
-64.476.528.896,504170,27518,06036.284.2
Anonymous submission
HumanBoxes
17.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
ViPeD
18.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
XYZvN
19.
0.80
69.378.52.594,44115,03720,12386.382.4
Anonymous submission
HBv20Rep
20.
0.80
69.178.13.297,94818,83016,61683.985.5
Anonymous submission
Detector APMODAMODPFAFTPFPFNPrecisionRecall
hbv20
21.
0.80
70.578.52.897,00016,28517,56485.684.7
Anonymous submission
XYZvX
22.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
SDP
23.
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.
ZIZOM
24.
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
25.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
HRCNdet
26.
0.86
-39.278.625.3104,927149,8849,63741.291.6
Anonymous submission
HRCN
27.
0.87
-53.678.528.2105,551166,9689,01338.792.1
Anonymous submission
meow_det2
28.
0.87
-105.678.138.4106,378227,3898,18631.992.9
Anonymous submission
meow_det
29.
0.88
-41.478.425.9105,644153,1308,92040.892.2
Anonymous submission
KDNT
30.
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.
Detector APMODAMODPFAFTPFPFNPrecisionRecall
YTLAB
31.
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.
PA_Det_NJ
32.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
UNV_Det
33.
0.89
81.979.02.1106,47812,7048,08689.392.9
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.