MOT17Det Results

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


DetectorAPMODA MODPFAFTPFPFNPrecisionRecall
YLHD
1.
0.27
-37.065.614.040,59882,93973,96632.935.4
mobilenet based human detection
MHD
2.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
YOLO_Virt
3.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
yolo_JTA
4.
0.62
54.371.52.375,84513,65838,71984.766.2
Anonymous submission
yolov3_ft
5.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
ACF
6.
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.
YLHDv2
7.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
ViPeD
8.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
yolov3
9.
0.77
62.974.73.794,14622,04720,41881.082.2
Anonymous submission
VDet
10.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
DetectorAPMODA MODPFAFTPFPFNPrecisionRecall
DPM
11.
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.
HDGP
12.
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.
v3
13.
0.52
41.077.01.857,55410,55157,01084.550.2
Anonymous submission
HumanBoxes
14.
0.79
58.077.34.995,65829,18618,90676.683.5
Anonymous submission
SDP
15.
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.
FRCNN
16.
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.
UNV_Det
17.
0.89
81.979.02.1106,47812,7048,08689.392.9
Anonymous submission
PA_MOT_Det
18.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
PA_Det_NJ
19.
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
VIS_DET1
20.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
DetectorAPMODA MODPFAFTPFPFNPrecisionRecall
ZIZOM
21.
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.
KDNT
22.
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.
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.

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.