A generic flexible and robust approach for intelligent real-time video-surveillance systems

Abstract

In this article we present a generic, flexible and robust approach for an intelligent real-time video-surveillance system. A previous version of the system was presented in [1]. The goal of these advanced tools is to provide help to operators by detecting events of interest in visual scenes, highlighting alarms and computing statistics. The proposed system is a multi-camera platform able to handle different standards of video inputs (composite, IP, IEEE1394 ) and which can basically compress (MPEG4), store and display them. This platform also integrates advanced video analysis tools, such as motion detection, segmentation, tracking and interpretation. The design of the architecture is optimized to playback, display, and process video flows in an efficient way for video-surveillance applications. The implementation is distributed on a scalable computer cluster based on Linux and IP network. It relies on POSIX threads for multitasking scheduling. Data flows are transmitted between the different modules using multicast technology and under control of a TCP-based command network (e.g. for bandwidth occupation control). We report here some results and we show the potential use of such a flexible system in third generation video surveillance systems. We illustrate the interest of the system in a real case study, which is the indoor surveillance.

Real-Time Image and Video Processing: From Research to Reality

“…Lately, digital video surveillance systems have become a high-priority topic of research worldwide [6,16,36,37,40,45,69,98,149,155]. Relevant technologies include automatic, robust face recognition [11,28,92,112,146], gesture recognition [111,142], tracking of human or object movement [9,40,61,68,76,92,102,151], distributed or networked video surveillance with multiple cameras [17,37,53,75], etc.…”