| 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 |
