Human Exclusive Alerts in CCTV Surveillance Using Computer Vision

This paper outlines a proposed approach to enhance modern surveillance systems by addressing the challenges posed by the excessive data generated by extensive CCTV camera networks. The proliferation of surveillance footage often results in inefficiencies, primarily due to false alarms triggered by non-human movements such as animals or environmental factors. The proposed system aims to tackle this issue by integrating advanced computer vision technologies with state-of-the-art deep learning algorithms, designed to accurately identify human presence and thereby significantly reduce irrelevant alerts Our intended solution involves the adoption of the latest YOLO (You Only Look Once) object detection model, YOLOv8, which is renowned for its efficiency and accuracy in real-time object recognition. We plan to combine this with OpenCV, a robust tool for image processing and computer vision tasks, to enhance our system's capability to process and analyze video data effectively. Furthermore, we aim to utilize the COCO dataset, a comprehensive object detection, segmentation, and captioning dataset, to train our deep learning algorithms. This training will equip our system to differentiate effectively between human and non-human elements by famil-iarizing it with a wide range of human poses and activities. The methodology we propose is currently undergoing rigorous validation and extensive real-world testing. We anticipate that these efforts will demonstrate the effectiveness of our approach in minimizing false alarms and maximizing the accuracy of human detection across various surveillance environments. By refining alert precision, our approach aims to enhance security measures, allowing surveillance operators to concentrate more on genuine human-related incidents. The research not only seeks to improve the operational efficiency of surveillance systems but also aims to contribute to the broader field of security technology by demonstrating potential applications of cutting-edge artificial intelligence in real-world scenarios