Smart Surveillance Framework: A Versatile Tool for Video Analysis

Computer Vision problems applied to visual surveillance have been studied for several years aiming at finding ac-curate and efficient solutions, required to allow the execu-tion of surveillance systems in real environments. The main goal of such systems is to analyze the scene focusing on the detection and recognition of suspicious activities per-formed by humans in the scene, so that the security per-sonnel can pay closer attention to these preselected activi-ties. To accomplish that, several problems have to be solved first, for instance background subtraction, person detection, tracking and re-identification, face recognition, 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. How-ever, in a real surveillance scenarios, the aforementioned problems have to be solved in sequence considering only videos as the input. Aiming at the direction of evaluat-ing approaches in more realistic scenarios, this work pro-poses a 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. 1