Real-Time Motion Estimation by Object-Matching for High-Level Video Representation

Motion estimation plays a key role in many video applications, such as frame-rate video conversion, video retrieval, video surveillance, and video compression. The key issue in these applications is to define appropriate representations that can efficiently support motion estimation with the required accuracy. In this paper, a low-complexity object motion estimation technique is proposed that is designed to fit the needs of high-level video representation such as in video surveillance or retrieval applications. In these applications, a representation of object motion in a way meaningful for high-level interpretation, such as event detection and classification, foregoes precision of estimation. The proposed method relies on the estimation of the displacements of the minimum bounding box (MBB) sides of an object. Two motion estimation steps are proposed: initial coarse estimation to find a single displacement for an object using the four sides of the MBB between two successive images and detection of non-translational motion and its estimation. The result is the detection of the type of object motion and the subsequent estimation of one or more motion values per object depending on the detected motion type. Special consideration is given to object motion in interlaced video and at image margin. Various simulations show that the proposed method provides a response in real-time and gives good estimates to use for object tracking, event detection, and highlevel video representation. The proposed object motion estimation method is insensitive to inaccurate segmentation in these applications.