Event monitoring system for automated incident detection

Abstract

Currently, computer vision technologies used in event monitoring systems to solve security problems in the field of transport, data protection, medicine are becoming an increasingly promising direction. Video surveillance sys-tems generate petabytes of data every day, and only a small part is used in processing. The use of video analytics will eliminate the need for storing and processing unnecessary data, their manual viewing, which will directly affect the cost, complexity and speed of solving operational production tasks of responding to incidents. The data from video cameras, information collected from different sources and used together for analysis would make it possible to more effectively and quickly identify and prevent various undesirable events. It is possible to automate the analysis of complex structured data, reducing the influence of the human factor, eliminating errors and abuses, using artificial intelligence methods, neural networks. But modern intelligent video analytics systems have drawbacks. Many systems are focused on the recognition of a certain type of images, can work in limited subject areas and under certain environmental conditions. Recognition algorithms are associated with a large number of false positives, especially in conditions of a rapidly increasing data volume, the degree of uncertainty of input information, therefore, it is proposed to supplement event monitoring systems. The systems contain a large number of settings and rules, which complicates the understanding of the system. There have been described the difficulties of using biometric data in recognition systems due to the legal restrictions, the main stages of designing an event monitoring system, its model, which combines elements of fuzzy logic and pattern recognition methods.