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