A Localized Approach to Abandoned Luggage Detection with Foreground-Mask Sampling
In this paper we propose a novel approach to the detection of abandoned luggage in video surveillance. Candidates of abandoned luggage items which may pose potential security threats are first identified and localized by our proposed foreground-mask sampling technique. Our approach can deal with luggage pieces of arbitrary shape and color without the need for prior learning, and it works well under crowded and highly-cluttered situations. This localization of suspicious luggage items in the scene enables us to focus attention and subsequent processing solely on their neighborhoods. The owner of the luggage is then located and tracked to determine whether or not the luggage has been abandoned deliberately. A probability model using the MAP principle is employed to calculate a posteriori confidence score for the luggage-abandonment event, and an alarm will be automatically triggered if the certainty of luggage abandonment is higher than a pre-defined threshold. We show our results on the video datasets provided by the 2007 IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS 2007) and the 2006 IEEE International Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2006).