Pedestrian detection based on the improved HOG features
Authors
Jianliang Meng, Shujin Li
Corresponding Author
Jianliang Meng
Available Online October 2015.
- 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/).
Cite this article
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 -