Segmentation and tracking of video objects for a content-based video indexing context

Abstract

This paper examines the problem of segmentation and tracking of video objects for content-based information retrieval. Segmentation and tracking of video objects plays an important role in index creation and user request definition steps. The object is initially selected using a semi-automatic approach. For this purpose, a user-based selection is required to define roughly the object to be tracked. In this paper, we propose two different methods to allow an accurate contour definition from the user selection. The first one is based on an active contour model which progressively refines the selection by fitting the natural edges of the object while the second used a binary partition tree with a marker and propagation approach. The video object is thus tracked by using a hybrid structure alternately combining a hierarchical mesh for the motion estimation between two frames and a multi-resolution active contour model. This contour model is derived directly from the mesh boundaries in order to reposition the snake's nodes onto the natural edges of the object. The object-based segmentation associated with object tracking allows relevant descriptors to be built for a future matching purpose.