Higher order statistical learning for vehicle detection in images
Ambasamudram Narayanan Rajagopalan, Philippe Burlina, Rama Chellappa · 1999
The paper describes a scheme for detecting vehicles in images. The proposed method approximately models the unknown distribution of the images of vehicles by learning higher order statistics (HOS) information of the 'vehicle class' from sample images. Given a test image, statistical information about the background is learnt 'on the fly'. An HOS-based decision measure then classifies test patterns as vehicles or otherwise. When tested on real images of aerial views of vehicular activity, the method gives good results even on complicated scenes. It does not require any a priori information about the site. However, it is amenable to augmentation with contextual information. The method can serve as an important step towards building an automated roadway monitoring system.