Authors:
Mohamed El Ansari
1
;
Redouan Lahmyed
1
and
Alain Tremeau
2
Affiliations:
1
Faculty of Science and University of Ibn Zohr, Morocco
;
2
University Jean Monnet, France
Keyword(s):
Pedestrian Detection, LIDAR Sensor, Visible Camera Sensor, Support Vector Machines (SVMs), Adaboost, Histogram of Oriented Gradients (HOG), Local Self-similarity (LSS).
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
This paper presents a hybrid pedestrian detection system on the basis of 3D LIDAR data and visible images
of the same scene. The proposed method consists of two main stages. In the first stage, the 3D LIDAR data
are classified to obtain a set of clusters, which will be mapped into the visible image to get regions of interests
(ROIs). The second stage classifies the ROIs (pedestrian/non pedestrian) using SVM as classifier and color
based histogram of oriented gradients (HOG) together with the local self-similarity (LSS) as features. The
proposed method has been tested on LIPD dataset and the results demonstrate its effectiveness.