We present a statistical pattern recognition scheme for detecting vehicles in still images. The methodology involves pattern classification using higher-order statistics (HOS) in a clustering framework. The proposed method approximately models the unknown distribution of the image patterns of vehicles by learning HOS information about the vehicle class from sample images. Given a test image, statistical information about the background is learned "on the fly." An HOS-based decision measure derived from a series expansion of the multivariate probability density function in terms of the Gaussian function and Hermite polynomials is used to classify test patterns as vehicles or otherwise. Experimental results on real images with cluttered background are given to demonstrate the performance of the proposed method. When tested on real aerial images, the method gives good results, even for complicated scenes. The detection rate is found to be quite good, while the false alarms are very few. The method can serve as an important step toward building an automated traffic monitoring system.
Higher-order-statistics-based detection of vehicles in still images
Journal of the Optical Society of America, Part A (Optics, Image Science and Vision) ; 18 , 12 ; 3037-3048
2001
12 Seiten, 20 Quellen
Article (Journal)
English
British Library Online Contents | 2016
|British Library Online Contents | 2016
|Context-based object detection in still images
British Library Online Contents | 2006
|