Real - time Anomaly Target Detection and Recognition in Intelligent Surveillance Systems based on SLAM

This paper introduces the application of Simultaneous Localization and Mapping (SLAM) technology in intelligent monitoring system. Traditionally, intelligent surveillance systems utilize image processing, pattern recognition, and computer vision technologies to filter out irrelevant information and automatically identify objects of interest. However, by incorporating SLAM technology into these systems, we are taking their capabilities to the next level. SLAM is a powerful tool that enables real-time spatial perception and mapping in dynamic environments. By applying SLAM technology to intelligent monitoring systems, we can improve the system's situational awareness, automate scene analysis, and realize the precise location of events. The combination of SLAM and intelligent surveillance systems enables these systems to autonomously identify and analyze critical information from surveillance video, effectively locate accident scenes, and detect anomalies with unprecedented speed and accuracy. At the same time, we also analyze the challenges and opportunities of integrating SLAM into these systems, and the direction of future research and development. Through comprehensive analysis, we aim to shed light on the transformational impact of SLAM technology on the capabilities of intelligent surveillance systems, as well as on the ability to drive real-time, round-the-clock surveillance.