Deep Learning-Based Smart Video Analytics for Automated Object Detection and Tracking

Smart video analytics systems use artificial intelligence and computer vision to automatically analyze video streams and detect important events in real time. This paper presents a simple and efficient smart video analytics system for object detection, tracking, and activity monitoring. The proposed system processes video frames using image preprocessing techniques and a deep learning-based object detection model to identify people and other objects. The detected objects are tracked across consecutive frames to monitor their movement and generate alerts for predefined events. The system improves surveillance by reducing manual monitoring, increasing detection accuracy, and enabling real-time decision-making. Experimental results show that the proposed approach provides reliable object detection and tracking with good accuracy and low processing time, making it suitable for security surveillance, traffic monitoring, and smart city applications.