MOT17Det Results

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


DetectorAPMODAMODPFAF TPFPFNPrecisionRecall
XYZv3
1.
0.00
-67.278.311.58768,309101,3650.10.1
Anonymous submission
cn
2.
0.00
-97.062.518.9891112,039113,6730.80.8
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.
HDGP
4.
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
5.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
MHD
6.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
VIS_DET1
7.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
YOLOv3_XYZ
8.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
DPM
9.
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.
YLHDv2
10.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
DetectorAPMODAMODPFAF TPFPFNPrecisionRecall
yolov3_ft
11.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
YOLO_Virt
12.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
FRCNN
13.
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.
Cas_CH_17
14.
0.65
51.580.05.189,43630,40425,11774.678.1
Anonymous submission
XYZvN
15.
0.80
69.378.52.594,44115,03720,12386.382.4
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.
HumanBoxes
17.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
SDP
18.
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.
ViPeD
19.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
hbv20
20.
0.80
70.578.52.897,00016,28517,56485.684.7
Anonymous submission
DetectorAPMODAMODPFAF TPFPFNPrecisionRecall
HBv20Rep
21.
0.80
69.178.13.297,94818,83016,61683.985.5
Anonymous submission
XYZvX
22.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
YTLAB
23.
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
24.
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.
UNV_Det
25.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
PA_Det_NJ
26.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
PA_MOT_Det
27.
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.