Title :
Pedestrian detection using covariance features
Author :
Said, Yahia ; Salah, Yahia ; Atri, Mohamed
Author_Institution :
Lab. of Electron. & Microelectron. (EμE), Univ. of Monastir, Monastir, Tunisia
Abstract :
Detecting pedestrians is a challenging problem owing to the motion of the subjects, the camera and the background and to variations in pose, appearance, clothing, illumination and background clutter. The Region Covariance Matrix (RCM) descriptors show experimentally significantly out-performs existing feature sets for pedestrian detection. In this paper, we present an efficient features extraction scheme: the Integral CovReg, inspired from Region Covariance Matrix (RCM) descriptors, combined with SVM classifier for pedestrian detection.
Keywords :
covariance matrices; feature extraction; image classification; object detection; support vector machines; Integral CovReg; SVM classifier; covariance features; feature extraction; image background; image motion; pedestrian detection; region covariance matrix descriptors; Conferences; Covariance matrices; Detectors; Feature extraction; Histograms; Support vector machines; Vectors; Image descriptor; Integral CovReg; Pedestrian detection; SVM;
Conference_Titel :
Image Processing, Applications and Systems Conference (IPAS), 2014 First International
Print_ISBN :
978-1-4799-7068-1
DOI :
10.1109/IPAS.2014.7043307
