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.
HumanBoxes
9.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
KDNT
10.
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.
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
MHD
11.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
PA_Det_NJ
12.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
PA_MOT_Det
13.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
SDP
14.
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.
UNV_Det
15.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
VDet
16.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
ViPeD
17.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
VIS_DET1
18.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
XYZv3
19.
0.00
-67.278.311.58768,309101,3650.10.1
Anonymous submission
XYZvN
20.
0.80
69.378.52.594,44115,03720,12386.382.4
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
XYZvX
21.
0.81
74.479.42.7101,19715,93013,36786.488.3
Anonymous submission
YLHDv2
22.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
yolov3_ft
23.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
YOLOv3_XYZ
24.
0.60
37.573.15.877,18234,26737,38269.367.4
Anonymous submission
YOLO_Virt
25.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
YTLAB
26.
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
27.
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.