Automatic detection of dangerous events for underground surveillance

This paper describes automatic video sequences processing techniques for detecting suspect and dangerous situations within public transportations. Proposed surveillance system is able to raise different kind of warnings and alarms on the basis of the particular detected situation. Algorithms used for objects detection and tracking will be described in details and performances will be discussed in relation with alarm conditions that are showed in the sequences that have been made available for this conference. An empty reference image is used for object extraction through image difference. In order to perform background updating a high level module is implemented taking into account the detected objects and their classification tags. The system has been tested on several sequences showing dangerous events due to human behaviors in a underground station.