Pedestrian detection based on the improved HOG features

DOI

10.2991/iwmecs-15.2015.139How to use a DOI?

Keywords

Pedestrian detection, MultiHOG, LBP, additive kernel SVM.

Abstract

For HOG features’ characteristics of high accuracy and large amount of calculation, selected MultiHOG features instead of traditional HOG by means of adjusting the structure of HOG features and using Fisher selection criteria. For the further detection effect, coalesced LBP feature which good at texture based on MultiHOG. The algorithm combining additive cross the SVM classifier to reduce the test time, improved the efficiency of detection and detected pedestrians sliding window. Finally, tested by INRIA standard data sets. The results showed that the algorithm has better feature detection and detection time than traditional one.

Copyright

© 2015, 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/).

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TY  - CONF
AU  - Jianliang Meng
AU  - Shujin Li
PY  - 2015/10
DA  - 2015/10
TI  - Pedestrian detection based on the improved HOG features
BT  - Proceedings of the 2015 2nd International Workshop on Materials Engineering and Computer Sciences
PB  - Atlantis Press
SP  - 697
EP  - 700
SN  - 2352-538X
UR  - https://doi.org/10.2991/iwmecs-15.2015.139
DO  - 10.2991/iwmecs-15.2015.139
ID  - Meng2015/10
ER  -