Method

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks [Faster R-CNN]
https://github.com/rbgirshick/py-faster-rcnn

Submitted on 23 Jan. 2016 23:55 by
Yu Xiang (Stanford University)

Running time:2 s
Environment:GPU @ 3.5 Ghz (Python + C/C++)

Method Description:
We applied the Faster R-CNN method to the KITTI
detection dataset.
Parameters:
We used 10 scales and 7 aspect ratios in the RPN
instead of 3 scales and 3 aspect ratios in the
original paper. The network architecture is
AlexNet.
Latex Bibtex:
@inproceedings{Ren2015NIPS,
author = {Shaoqing Ren and
Kaiming He and
Ross B. Girshick and
Jian Sun},
title = {Faster {R-CNN:} Towards Real-
Time
Object Detection with Region Proposal
Networks},
booktitle = NIPS,
year = {2015},
url =
{http://arxiv.org/abs/1506.01497},
timestamp = {Wed, 01 Jul 2015 15:10:24
+0200},
biburl = {http://dblp.uni-
trier.de/rec/bib/journals/corr/RenHG015},
bibsource = {dblp computer science
bibliography, http://dblp.org}
}

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
Car (Detection) 87.90 % 79.11 % 70.19 %
Pedestrian (Detection) 78.35 % 65.91 % 61.19 %
Cyclist (Detection) 71.41 % 62.81 % 55.44 %
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



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




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