MOT17Det Results

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


DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
PA_Det_NJ
1. new
0.89
82.179.32.4108,48014,4176,08488.394.7
PA_TECH_NJ
YTLAB
2.
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
3.
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.
PA_MOT_Det
4.
0.86
83.479.22.2108,58113,0715,98389.394.8
Anonymous submission
ZIZOM
5.
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
6.
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
7.
0.80
75.474.11.896,88410,48917,68090.284.6
Anonymous submission
yolov3
8.
0.77
62.974.73.794,14622,04720,41881.082.2
Anonymous submission
FRCNN
9.
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
10.
0.71
63.970.52.286,42913,25428,13586.775.4
Anonymous submission
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
yolov3_ft
11.
0.70
60.271.82.382,76713,80931,79785.772.2
Anonymous submission
yolo_JTA
12.
0.62
54.371.52.375,84513,65838,71984.766.2
Anonymous submission
DPM
13.
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.
HumanBoxes
14. new
0.61
47.074.03.775,50521,60939,05977.765.9
Anonymous submission
VIS_DET1
15.
0.54
58.679.80.670,6183,51643,93595.361.6
Anonymous submission
v3
16.
0.52
41.077.01.857,55410,55157,01084.550.2
Anonymous submission
MHD
17.
0.49
11.269.98.864,63751,80149,92755.556.4
Mobilenet-based Human Detection
YLHDv2
18.
0.46
56.973.22.580,09314,93834,47184.369.9
https://arxiv.org/abs/1612.08242
HDGP
19.
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
20.
0.44
44.775.71.056,9805,76557,58490.849.7
Vitrociset Detection Algorithm
DetectorAPMODAMODPFAFTPFPFNPrecisionRecall
ACF
21.
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.
YLHD
22.
0.27
-37.065.614.040,59882,93973,96632.935.4
mobilenet based human detection

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.