DocumentCode :
3579980
Title :
Deformable parts model for people detection in heavy machines applications
Author :
Manh-Tuan Bui ; Fremont, Vincent ; Boukerroui, Djamal ; Letort, Pierrick
Author_Institution :
Heudiasyc, Univ. de Technol. de Compiegne (UTC), Compiegne, France
fYear :
2014
Firstpage :
389
Lastpage :
394
Abstract :
In this paper we focus on the evaluation of the deformable part model (DPM) proposed by Felzenszwalb et al. [IO] in the context of vision-based people detection in heavy machines applications. The proposed system uses a single fisheye camera to provide a wide field-of-view (FOV) at low cost. However, the fisheye optical distortions present several difficulties for image processing and object recognition. The DPM approach shows important flexibility when dealing with varying object´s form. It gives good performances on people detection when images present strong fisheye distortions. Base on the analysis of DPM in the context of fisheye image, we proposed an adaptive detector which is more suitable.
Keywords :
computer vision; object detection; object recognition; DPM approach; FOV; adaptive detector; deformable parts model; fisheye optical distortions; heavy machines applications; image processing; object recognition; single fisheye camera; vision-based people detection; wide field-of-view; Cameras; Context; Deformable models; Detectors; Optical distortion; Training; Vectors; Heavy machines; deformable part model; fisheye images; histogram of oriented gradients; latent support vector machine; pedestrian detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on
Type :
conf
DOI :
10.1109/ICARCV.2014.7064337
Filename :
7064337
Link To Document :
بازگشت