The algorithm for recognizing situations in a distributed video surveillance system
CCTV systems are the most important means for preventing and timely handling of contingencies, such as crimes, emergency situations. A big number of cameras and a large control area makes it necessary to introduce video analytics to recognize dangerous situations. In this case it is necessary to take into account the data from a number of video cameras both for detecting a motion path of a recognized object and for increasing recognition reliability. The article proposes an algorithm for recognizing emergencies for a distributed video surveillance system based on stochastic grammars. Recognition of a situation occurs at 3 levels: images are recognized at the lower level, events are recognized at the average level, and situations are recognized at the top. To reduce the system response time, it is proposed to use a multi-agent architecture that allows distributing the load between intelligent cameras. Data exchange occurs only between nearby nodes, so network traffic reduces. The use of a large number of cameras involves zones controlled by several nodes. Combination of detection results from several cameras makes it possible to increase the estimate reliability. However, it is required to know the mutual arrangement of chambers and the angles of their turns. The article suggests some methods for automatic calibration of cameras in a distributed video surveillance system, ways of combining images from different cameras, in particular, based on speed rate vectors of objects. Taking into account certain features of a distributed video surveillance system, there is a developed algorithm for recognizing emergencies for an intelligent surveillance camera. Each camera generates probable situations based on previously recognized events. When a threshold value of probabilistic evaluation of the detection result is exceeded, its refinement is carried out in the process of interaction with neighboring nodes.