Implementation of an Outdoor Camera Sabotage Detection System Model

This study introduces an innovative camera-tamper monitoring system designed for outdoor surveillance, addressing the limitations of human monitoring. Tailored for large-scale camera systems, the system employs deep learning algorithms to identify and categorize frequent tampering events such as defocus, occlusion, and changes in orientation. The system operates in real-time, offering visual feedback through a user-friendly web portal and sending tampe r notifications via SMS. By utilizing convolutional neural networks optimized through training, the system effectively reduces reliance on human operators, mitigating the risk of human error. Security personnel can act immediately to stop potential security breaches and the loss of important surveillance footage through classified sabotage events. This method enhances the dependability and efficiency of the monitoring process, ultimately fortifying the security of outdoor surveillance infrastructure. The outcomes highlight the system's potential to improve monitoring capabilities and reduce safety risks in extensive outdoor camera installations. This paper discusses the training process and compares the outcome of the system with the existing research outcomes in the relative area. Furthermore, it presents results obtained from various experiments throughout the way to completion and proposes future extensions that can be integrated into the system.