Automatic Frame-Cut Detection for Self-Diagnostics of Video Surveillance Systems

There are thousands of hours of digital video recorded everyday by surveillance cameras, dashboard cameras, police body cameras, that are often used as court evidence. These video recordings could have been edited before being archived and there is no easy way to know if they have been tampered with unless visually and carefully examined. A jump-cut could also be the result of faulty camera sensor, processing software failure, or transmission error. With the vast application span of security and surveillance cameras from banks, airports, borders, ports of entry, high value government properties such as embassies, it is important to have video security or surveillance system capable of self-diagnostics. In this research, we propose an automatic method that can examine video recordings and detect frame-cuts without any human intervention. Our algorithm could assist law enforcement, department of justice, department of home land security, customs and border protection, and other government agencies to examine thousands of hours of video recordings quickly and autonomously and produce the list of possible frame cuts with their timestamps.