Journal of Robotics, Networking and Artificial Life

Volume 1, Issue 3, December 2014, Pages 184 - 188

Comparing Effectiveness of Feature Detectors in Obstacles Detection from a Video

Authors
Shaohua Qian, Joo Kooi Tan, Hyoungseop Kim, Seiji Ishikawa, Takashi Morie, Takashi Shinomiya
Corresponding Author
Shaohua Qian
Available Online 15 December 2014.
DOI
https://doi.org/10.2991/jrnal.2014.1.3.3How to use a DOI?
Keywords
Feature detectors, Harris, SIFT, SURF, FAST, car vision
Abstract

We have already proposed an obstacles detection method using a video taken by a vehicle-mounted monocular camera. In this method, correct obstacles detection depends on whether we can accurately detect and match feature points. In order to improve the accuracy of obstacles detection, in this paper, we make comparison among four most commonly used feature detectors; Harris, SIFT, SURF and FAST detectors. The experiments are done using our obstacles detection method. The experimental results are compared and discussed, and then we find the most suitable feature point detector for our obstacles detection method.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Journal
Journal of Robotics, Networking and Artificial Life
Volume-Issue
1 - 3
Pages
184 - 188
Publication Date
2014/12/15
ISSN (Online)
2352-6386
ISSN (Print)
2405-9021
DOI
https://doi.org/10.2991/jrnal.2014.1.3.3How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Shaohua Qian
AU  - Joo Kooi Tan
AU  - Hyoungseop Kim
AU  - Seiji Ishikawa
AU  - Takashi Morie
AU  - Takashi Shinomiya
PY  - 2014
DA  - 2014/12/15
TI  - Comparing Effectiveness of Feature Detectors in Obstacles Detection from a Video
JO  - Journal of Robotics, Networking and Artificial Life
SP  - 184
EP  - 188
VL  - 1
IS  - 3
SN  - 2352-6386
UR  - https://doi.org/10.2991/jrnal.2014.1.3.3
DO  - https://doi.org/10.2991/jrnal.2014.1.3.3
ID  - Qian2014
ER  -