A Smart Resource-efficient Surveillance System

The excessively increasing demand on bandwidth resources entails many challenges in terms of acquiring data while saving its acquisition resources. Surveillance systems are of those vital applications that consume data bandwidth. In this paper, a smart prototype surveillance system is proposed using machine learning techniques as a mean of saving bandwidth by transmitting only the significant information through the system's resources. The proposed system operates in two modes; motion detection and face recognition. Python environment and some of relevant libraries were used to establish the prototype. Promising and interesting results were obtained in terms of bandwidth and storage saving.