Detection of Unauthorised photography in Prohibited places through CCTV using Machine Learning - A. Jeba Sheela (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Unauthorized photography is one of the most common and less accountable crimes. Because more and more people have access to a wider range of photographic technologies, it is becoming more difficult to ensure improved security and privacy protection in a number of contexts. Points of interest include company headquarters, government buildings, research centers, military stations, and other private areas where protecting privacy and avoiding illegal data collection are crucial. A YOLO based model helps to reduce the impact of such crimes and preserve privacy while the current surveillance infrastructure. In order to prevent unwanted data collection, the YOLO classifier in conjunction with the MoveNet for action detection offers a workable solution that promotes a more secure society. The societal impact of this technology is substantial, as it addresses growing concerns related to privacy infringement, industrial espionage, and unauthorized data gathering. With the help of the DarkNet backbone and Adam Optimiser an efficient system with a mean average precision of 89.7% and a mAP50-95 of 80% is achieved swiftly in around 150ms. Additional by using YOLOv8s and YOLOv8m parameter accuracy is improved by 2.1% and 3.3% respectively against the more lightweight and faster YOLOv8n. Objectness scores are penalized and scored based on the Binary Cross-Entropy Loss to improve object localization.