People and luggage recognition in airport surveillance under real-time constraints

This paper describes an approach to classify people, groups of people and luggage in the halls of an airport. The algorithm is included into a surveillance system which tracks and classifies objects and transmits this information to a higher computational level which fuses the information of several cameras covering overlapping areas. Two kind of features are used: foreground density features and features related to real-size of objects, obtained by applying a homographic model. A classification schema based on k-nn classifiers and a voting system makes the classification process highly robust. On-line and off-line experiments are introduced.