Abstract
Nowadays, colossal numbers of facilities all over the world are protected from various types of threats by video surveillance cameras. Video surveillance systems are in urgent and increasing demand to ensure people, things and places security. These networks represent a huge amount of video to be transmitted, viewed and analyzed. However, current surveillance methodologies become increasingly ineffective as the number of cameras grows. To remedy this issue, we propose appending a pre-analysis stage to the video surveillance system. This stage extracts, compresses and sends only the information of interest from the captured video data. Through this paper, scalability, which has always been treated as a processing tool in video surveillance, is handled otherwise to generate more efficient and adaptable video surveillance systems. The pre-analysis phase works in a scalable way. It narrows down the analyzed data by extracting just the useful information depending on the final desired application. The originality of this work lies in the combination of the pre-analysis and the scalability to generate a progressive scalable video surveillance architecture responding to the end user needed application. The principal contributions mentioned in this paper are: i) A framework for a scalable application-oriented architecture for video surveillance systems; ii) A region of interest simplification method aiming to filter in a spatio-temporal way the captured data; iii) A region of interest modeling technique which simplifies the moving objects' representation in the image plane. The evaluation step shows promising results and demonstrates the effectiveness of the suggested architecture.