| dc.description.abstract | The study presents a newly created camera-tampering detection system for outdoor cameras, aiming to
overcome the boundaries of human monitoring. It is
intended to be implemented in large scale camera systems
to identify frequent tampering events like defocus, occlusion
and changes in orientation, and provide real time alerts and
visual feedback through a user friendly web portal designed
especially for this purpose. The system can effectively
recognize and categorize tampering instances by utilizing
deep learning algorithms, which reduces dependency on
human operators and lowers the risk of human mistake. To
detect and categorize tampering, three algorithms are
utilized, and the features of each algorithm are listed.
Security staff can take the necessary measures to stop
potential security breaches or the loss of important
surveillance footage by quickly identifying tampering
occurrences. The suggested method strengthens the
monitoring process’s dependability and efficiency, which in
turn strengthens the security of the outdoor surveillance
infrastructure. | en_US |
