MOT17Det Results

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


DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
AInnoDetV2
1.
0.89
-58.479.129.5107,733174,6086,83138.294.0
AInnovation: PC Attention Net
UNV_Det
2.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
PA_Det_NJ
3.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
YTLAB
4.
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.
KDNT
5.
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.
F_ViPeD_B
6.
0.89
-14.477.420.8106,698123,1947,83146.493.2
L. Ciampi, N. Messina, F. Falchi, C. Gennaro, G. Amato. Virtual to Real adaptation of Pedestrian Detectors for Smart Cities. In arXiv e-prints, 2020.
ISE_MOTDet
7.
0.88
66.579.75.0105,53429,3549,03078.292.1
Anonymous submission
motrcnn
8.
0.88
65.979.85.1105,80130,3258,76377.792.4
Anonymous submission
ISE_Detv2
9.
0.88
67.479.94.9106,09428,8658,47078.692.6
MIFD
cascade_R
10.
0.88
-229.380.762.6107,820370,5226,74422.594.1
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
meow_det
11.
0.88
-41.478.425.9105,644153,1308,92040.892.2
Anonymous submission
meow_det2
12.
0.87
-105.678.138.4106,378227,3898,18631.992.9
Anonymous submission
HRCN
13.
0.87
-53.678.528.2105,551166,9689,01338.792.1
Anonymous submission
HRCNdet
14.
0.86
-39.278.625.3104,927149,8849,63741.291.6
Anonymous submission
PA_MOT_Det
15.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
ZIZOM
16.
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.
SDP
17.
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.
XYZvX
18.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
hbv20
19.
0.80
70.578.52.897,00016,28517,56485.684.7
Anonymous submission
HBv20Rep
20.
0.80
69.178.13.297,94818,83016,61683.985.5
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
XYZvN
21.
0.80
69.378.52.594,44115,03720,12386.382.4
Anonymous submission
ViPeD
22.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
HumanBoxes
23.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
RTFCP
24.
0.76
-64.476.528.896,504170,27518,06036.284.2
Anonymous submission
VSPD
25.
0.75
47.676.47.498,25343,77116,31169.285.8
Anonymous submission
Strong_SSD
26.
0.72
53.477.04.990,03628,85424,52875.778.6
Anonymous submission
FRCNN
27.
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.
YOLO_Virt
28.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
yolov3_ft
29.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
Cas_CH_17
30.
0.65
51.580.05.189,43630,40425,11774.678.1
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
DPM
31.
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.
YOLOv3_XYZ
32.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
cascade
33.
0.57
-92.972.932.183,586190,04430,97830.573.0
Anonymous submission
VIS_DET1
34.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
muDetectV3
35.
0.50
40.076.22.157,93712,15656,62782.750.6
Anonymous submission
MHD
36.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
YLHDv2
37.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
HDGP
38.
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
39.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
muDetect
40.
0.43
42.076.81.355,9527,83458,61287.748.8
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
muDetectV2
41.
0.39
38.476.82.558,66414,63655,90080.051.2
Anonymous submission
ACF
42.
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.
cn
43.
0.00
-97.062.518.9891112,039113,6730.80.8
Anonymous submission
XYZv3
44.
0.00
-67.278.311.58768,309101,3650.10.1
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.