Carried object detection using star skeleton with adaptive centroid and time series graph

In this paper, we introduce a novel method to detect a carried object seen from a stationary camera using human body silhouette feature information. We use star skeletonization technique with the adaptive centroid point to extract human feature. The carried object is classified using time series of motions of the extracted skeleton limbs. The boundary of the carried object is figured from carried object's track points and adjacent sink curves of contour. The method is able to detect and track carried object such as a luggage and a backpack. Moreover, we also achieve the detection of leaving luggage event. We perform experiments using some data from TRECVID dataset and manually captured data.