Nighttime Pedestrian Detection Using Local Oriented Shape Context Descriptor

DOI

10.2991/iccsee.2013.231How to use a DOI?

Keywords

Nighttime Pedestiran Detection, Local Oriented Shape Context Descriptor, HOG, SVM, Night-Vision Camera

Abstract

In this paper, we propose a novel feature based on local oriented shape context (LOSC) descriptor for nighttime pedestrian detection. Shape context descriptor is widely used in object recognition but not making a good performance in complex situations because it does not consider the edge’s orientation. To address this limitation, our method adds orientation information to the shape context descriptor. We first compute the image gradient in nine directions and then extract shape context descriptor in each direction. Finally we put the feature vector to linear SVM for training. We tested this descriptor’s performance on our nighttime pedestrian samples captured by normal night-vision camera. The experiment results show that our method achieved high detection rate and had fewer dimensions than the HOG descriptor.

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)

TY  - CONF
AU  - Guoliang Li
AU  - Yong Zhao
AU  - Daimeng Wei
AU  - Ruzhong Cheng
PY  - 2013/03
DA  - 2013/03
TI  - Nighttime Pedestrian Detection Using Local Oriented Shape Context Descriptor
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 917
EP  - 920
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.231
DO  - 10.2991/iccsee.2013.231
ID  - Li2013/03
ER  -