Hierarchical representation of scenes using activity information

Object segmentation and tracking are two key issues in the analysis of scenes for video surveillance or scene understanding applications. This paper addresses the object segmentation task by presenting a new algorithmic contribution in these applications' context. The proposed method combines an adaptive background learning technique with a hierarchical segmentation method based on Binary Partition Trees. The result is a region-based dynamic scene description, where each active region is characterized by a temporal feature, reflecting on the time it remains in the same position of the scene. This description is then used to classify the background and foreground objects of the scene and can also be used as an additional feature for region tracking and scene understanding.