Real Time Smart Surveillance using Motion Analysis

In the last years, smart surveillance has been one of the most active research topics in computer vision because of the wide spectrum of promising applications. Its main point is about the use of automatic video analysis technologies for surveillance purposes. In general, a processing framework for smart surveillance consists of a preliminary motion detection step in combination with high-level reasoning that allows automatic understanding of evolutions of observed scenes. In this paper, we propose a surveillance framework based on a set of reliable visual algorithms that perform different tasks: a motion analysis approach that segments foreground regions is followed by three procedures, which perform object tracking, homographic transformations and edge matching, in order to achieve the real-time monitoring of forbidden areas and the detection of abandoned or removed objects. Several experiments have been performed on different real image sequences acquired from a Messapic museum (indoor context) and the nearby archaeological site (outdoor context) to demonstrate the effectiveness and the flexibility of the proposed approach.