In the last years, the number of surveillance cameras placed in public locations has increase vastly and as consequence, a huge amount of visual data is generated every minute. In general, this data is analyzed manually, a challenging task which is labor intensive and prone to errors. Therefore, automatic approaches must be employed to enable the processing of the data, so that human operators only need to reason about selected portions. Computer vision problems focused on solving problems in the domain of visual surveillance have been developed 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 performed by humans in the scene, so that the security staff can pay closer attention to these preselected activities. However, these systems are rarely tackled in a scalable manner. Before developing a full surveillance system, several problems have to be solved, which are usually solved individually. However, in a real surveillance scenario, these problems have to be solved in sequence considering only videos as the input. With that in mind, this work proposes a framework for scalable video analysis called Smart Surveillance Framework (SSF) to allow researchers to implement their solutions to the surveillance problems as a sequence of processing modules that communicates through a shared memory. 
A scalable and flexible framework for smart video surveillance