A scalable and versatile framework for smart video surveillance

Abstract

Computer Vision problems applied to visual surveillance have been studied for several years aiming at finding accurate and efficient solutions. The main goal of such systems is to analyze the scene focusing on the detection and recognition of suspicious activities. To accomplish that, several problems have to be solved first, for instance background subtraction, person detection, tracking, and action recognition. Even though each of these problems have been researched in the past decades, they are hardly considered in a sequence, each one is usually solved individually. However, in a real surveillance scenarios, the aforementioned problems have to be solved in sequence. Aiming at the direction of evaluating approaches in more realistic scenarios, this work proposes a scalable framework called Smart Surveillance Framework (SSF), to allow researchers to implement their solutions to the above problems as a sequence of processing modules that communicate through a shared memory.