Abstract
Human detection is exploited as a key operation in many applications such as automotive safety, intelligent vehicles, assisted living, and video surveillance. Consequently, there is a significant advancement in this area of research in the past years and a vast literature. In this study, the authors propose a pedestrian detection system which relies on sliding covariance matrix feature descriptor combined with a support vector machine classifier. The proposed framework is implemented onto field programmable gate array prototyping boards. Experimental results using the standard Institut National de Recherche en Informatique et en Automatique (INRIA) pedestrian benchmark dataset show that the proposed architecture achieved outstanding processing performances with high detection accuracy when compared with state-of-the-art methods.