An embedded knowledge extraction technology for consumer video surveillance

New advances in embedded computing technology have opened up the potential for new era of consumer surveillance systems. This paper will explore and propose a new embedded modeling technique for the configuration of consumer video surveillance systems that can identify events of interest, especially on abandoned and stolen objects in indoor and outdoor environments. The proposed embedded system will focus on high level behavior understanding for object detection, tracking and classification. The experimental results illustrate the ability of the system to create complex spatiotemporal relations and to recognize the behavior of one or multiple objects in various video scenes.