On any given day, a large airport will see hundreds of thousands of people filtering in and out of different gates. Amidst the clutter of passengers, it becomes difficult for security and TSA officials to monitor every individual for unusual behavior. The Video Analytics Surveillance Transition Project (VAST) is attempting to assist TSA officers by supplying a ‘Tag-and-Track’ algorithm to follow the tagged person’s movements through the airport camera system. The ability to know where a person of interest is at all times would save the airport money on unnecessary shutdowns and heighten security. For example, someone attempting to walk the wrong way through an exit lane. Using video anomaly sensing technology, VAST researchers created an algorithm to identify counter-flow movement in the exit lane camera field of view. This algorithm has performed well to identify and ‘tag’ people trying to enter the secure area through the exit lane. Research is now focused on re-identifying that person across multiple camera views. Researchers need a “ground truth” for testing and development of automatic tracking algorithms to rate performance. The ground truth software interpolates between frames, allowing for a manually created ‘track’ of each person. Individual researchers go through each camera view, re-identifying the tracked IDs through the different camera views. Implementing video anomaly sensing and tracking technology in airports across the United States will be extremely beneficial for future airport security. This technology can save airports money and prevent delays for passengers by recognizing potential threats before any breach occurs.