Reliable Detection of Security Camera Misalignment in Urban Rail Environments

Cameras are substantial elements to provide security to passengers in public spaces, e.g. in urban subway environments. Automated image processing algorithms are used more and more often to analyze the cameras’ video streams. However, misaligned cameras may produce serious problems by either generating false alarms or even being blind due to a shifted field of view. This paper presents two simple, real-time, and straight-forward software methods to detect camera movement and also allow for an adjustable tolerance, i.e. small changes are acceptable. The first approach shifts a reference edge (from a master image) along a convenient path; the second method uses distance measuring to detect a critical camera movement. Both methods are tested on real-world video from a subway environment. Since the algorithms are proposed for application in a railway environment with typically high requirements on operational robustness and reliability, special emphasis is put on constraints and limitations of the presented algorithms.