Fast Pedestrian Detection Using a Night Vision System for Safety Driving

This paper proposes a rapid pedestrian-detection algorithm for thermal images by using energy symmetry and oriented center-symmetric local binary pattern (OCS-LBP) features based on luminance saliency. During preprocessing, energy symmetry based on luminance saliency is used as the filter to remove...

Full description

Saved in:
Bibliographic Details
Main Authors: Mi Ra Jeong, Jun-Yong Kwak, Jung Eun Son, ByoungChul Ko, Jae-Yeal Nam
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Classification algorithms
Feature extraction
Pedestrian detection
thermal image
luminance saliency
energy symmetry
random forest
Radio frequency
Thermal energy
Thermal sensors
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes a rapid pedestrian-detection algorithm for thermal images by using energy symmetry and oriented center-symmetric local binary pattern (OCS-LBP) features based on luminance saliency. During preprocessing, energy symmetry based on luminance saliency is used as the filter to remove objects and to reduce the pedestrian classification time. The OCS-LBP feature is then extracted from a candidate window and subjected to a random forest classifier (RF) that separates candidate windows into pedestrian and non-pedestrian classes. The proposed algorithm has been successfully applied to various thermal images captured in a car, and its detection performance is better than that of other methods.
DOI:10.1109/CGiV.2014.25