Method

CFM [CFM]


Submitted on 6 Mar. 2016 15:30 by
Qichang Hu (The University of Adelaide)

Running time:<2 s
Environment:GPU @ 2.5 Ghz (Matlab + C/C++)

Method Description:
No description
Parameters:
No parameters
Latex Bibtex:
@ARTICLE{7807316,
author={Q. Hu and P. Wang and C. Shen and A. van
den Hengel and F. Porikli},
journal={IEEE Transactions on Circuits and
Systems for Video Technology},
title={Pushing the Limits of Deep CNNs for
Pedestrian Detection},
year={2017},
volume={PP},
number={99},
pages={1-1},
keywords={Australia;Detectors;Feature
extraction;Labeling;Object
detection;Proposals;Training;Pedestrian
detection;convolutional feature map
(CFM);ensemble model},
doi={10.1109/TCSVT.2017.2648850},
ISSN={1051-8215},
month={},}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Pedestrian (Detection) 74.76 % 62.84 % 56.06 %
This table as LaTeX

2D object detection results.
This figure as: png eps pdf txt gnuplot




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