Pedestrian detection using histograms of Oriented Gradients of granule feature
Journal
IEEE Intelligent Vehicles Symposium, Proceedings
Pages
1410-1415
Date Issued
2013
Author(s)
Abstract
To robustly detect people in a video sequence is hard due to various challenges. One of the most successful discriminative features for finding people goes to the Histograms of Oriented Gradients (HOG). Although the major contour information is encoded in the HOG feature well, the background clutter disturbs the gradient information. Thus, an extension of HOG, called histograms of oriented gradient of granules (HOGG), is proposed. Instead of collecting gradient information at each pixel, the histograms of gradients in small regions are computed. HOGG with different granularity can describe the contour while ignoring the noisy edges. Moreover, the clutter background problem can be solved by encoding extra region information. With the help of the integral image technique, the evaluation of HOGG can be efficient. The final HOG+HOGG classifier obtains 92% detection rate at 10-4 false positive per window in the experiments. © 2013 IEEE.
Event(s)
2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
SDGs
Other Subjects
Clutter background; Contour information; Different granularities; Discriminative features; Gradient informations; Histograms of oriented gradients; Histograms of oriented gradients (HoG); Pedestrian detection; Granulation; Intelligent vehicle highway systems; Object recognition; Graphic methods
Type
conference paper