Method

IVA [IVA]
https://github.com/ShaoqingRen/faster_rcnn

Submitted on 30 Jun. 2016 04:46 by
yousong zhu (Chinese Academy of Sciences, Institute of Automation)

Running time:0.4 s
Environment:GPU @ 2.5 Ghz (C/C++)

Method Description:
ACCV 2016, Scale-adaptive deconvolutional
regression, based on Faster RCNN
Parameters:
Scale-adaptive deconvolutional regression
Latex Bibtex:
@inproceedings{Zhu2016ACCV,
title={Scale-adaptive Deconvolutional
Regression Network for Pedestrian Detection},
Author = {Zhu, Yousong and Wang, Jinqiao
and Zhao, Chaoyang and Guo, Haiyun and
Lu, Hanqing},
booktitle = {ACCV},
Year = {2016}
}
@inproceedings{ren2015faster,
title={Faster R-CNN: Towards real-time object
detection with region proposal networks},
author={Ren, Shaoqing and He, Kaiming and
Girshick, Ross and Sun, Jian},
booktitle={Advances in neural information
processing systems},
pages={91--99},
year={2015}
}

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) 83.03 % 70.63 % 64.68 %
Cyclist (Detection) 77.63 % 67.36 % 59.62 %
This table as LaTeX


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



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




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