The bar graph shows a moderate amount of outlier pixels at the beginning, but these are simply noise pixels. Although there is a small further increase when the subject walks into the image, the difference drops to noise level as the subject continues to enter. The differences do not become significantly different from noise until the subject walks directly in front of the camera. This makes sense because when the subject is far from the camera, his disruption of the image is about the same as other noise, but when he gets close, he is disrupting many more pixels. A security system like this would present two problems. Either the system is triggered by noise, causing many false alarms, or the system might not detect potential threats, especially if the threat has the sense of mind not to walk directly in front of the camera.

The bar graph for v2 does not show a significant increase in pixels above noise level until frame 11 or so, when the subject had just begun to enter the image. After that, the number of pixels stays above noise level until the subject goes off-camera. Hence, the system does work better with the second image, although the resulting system is still not good enough for security, especially if the subject were to be seen in only a small portion of the camera's image, and any decent intruder would take the time to case out the camera locations as to minimize detection. Hence, the system could still be easily fooled by professionals. The difference in the background images were mainly in the positions that the person was in, as well as certain objects on the desk which may have had their lighting significantly changed during the entry.

