Back to articles
Articles
Volume: 28 | Article ID: art00002
Image
Histogram of Oriented Phase and Gradient (HOPG) Descriptor for Improved Pedestrian Detection
  DOI :  10.2352/ISSN.2470-1173.2016.3.VSTIA-511  Published OnlineFebruary 2016
Abstract

This paper presents a new pedestrian detection descriptor named Histogram of Oriented Phase and Gradient (HOPG) based on a combination of the Histogram of Oriented Phase (HOP) features and the Histogram of Oriented Gradient features (HOG). The proposed descriptor extracts the image information using both the gradient and phase congruency concepts. Although the HOG based method has been widely used in the human detection systems, it lacks to deal effectively with the images impacted by the illumination variations and cluttered background. By fusing HOP and HOG features, more structural information can be identified and localized in order to obtain more robust and less sensitive descriptors to lighting variations. The phase congruency information and the gradient of each pixel in the image are extracted with respect to its neighborhood. Histograms of the phase congruency and the gradients of the local segments in the image are computed with respect to its orientations. These histograms are concatenated to construct the HOPG descriptor. The performance evaluation of the proposed descriptor was performed using INRIA and DaimlerChrysler datasets. A linear support vector machine (SVM) classifier is used to train the pedestrians. The experimental results show that the human detection system based on the proposed features has less error rates and better detection performance over a set of state of the art feature extraction methodologies.

Subject Areas :
Views 26
Downloads 0
 articleview.views 26
 articleview.downloads 0
  Cite this article 

Hussin K Ragb, Vijayan K Asari, "Histogram of Oriented Phase and Gradient (HOPG) Descriptor for Improved Pedestrian Detectionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Video Surveillance and Transportation Imaging Applications,  2016,  https://doi.org/10.2352/ISSN.2470-1173.2016.3.VSTIA-511

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2016
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA