Smart Surveillance System

Smart surveillance systems leverage computer vision and machine learning to analyze video feeds, aiming to enhance security and situational awareness. Current research emphasizes real-time object detection and identification, often employing deep learning architectures like YOLO for improved accuracy and efficiency, alongside techniques for background subtraction using methods such as Arithmetic Distribution Neural Networks (ADNN). A growing focus involves moving beyond simple signal detection to inferring the underlying causes of observed events, enabling more intelligent and context-aware responses. This field holds significant potential for improving public safety, security infrastructure, and resource management in various settings.