Energy Feature Integration for Motion Segmentation

This chapter deals with the problem of segmentation of apparent-motion. Apparent-motion segmentation can be stated as the identification and classification of regions undergoing the same motion pattern along a video sequence. Motion segmentation has a great importance in robotic applications such as autonomous navigation and active vision. In autonomous navigation, motion segmentation is used in identifying mobile obstacles and estimating their motion parameters to predict trajectories. In active vision, the system must identify its target and control the cameras to track it. Usually, segmentation is based on some low level feature describing the motion of each pixel in a video frame. So far, the variety of approaches to deal with the problems of motion feature extraction and motion segmentation that has been proposed in literature is huge. However, all of them suffer from different shortcomings and up to date there is no completely satisfactory solution. Recent approaches to motion segmentation include, for example, that of Sato and Aggarwal (Sato & Aggarwal, 2004), where they define the Temporal Spatio-Velocity (TSV) transform as a Hough transform evaluated over windowed spatio-temporal images. Segmentation is accomplished by thresholding of the TSV image. Each resulting blob represents a motion pattern. This solution has proved to be very robust to occlusions, noise, low contrast, etc. Its main drawback is that it is limited to translational motion with constant velocity. It is very common to use a Kalman filter to estimate velocity parameters from intensity observations (Boykov & Huttenlocher, 2000). Kalman filtering alone presents severe problems with occlusions and abrupt changes, like large inter-frame displacements or deformations of the object. If a prior model is available, the combined use of Kalman filtering and template matching is the typical approach to deal with occlusions. For instance, Kervrann and Heitz (1998) define an a priori model with global and local deformations. They apply matching with spatial features for initialization and reinitialization of global rigid transformation and local deformation parameters in case of abrupt changes and Kalman filtering for tracking otherwise. Nguyen and Smeulders (2004) perform template matching and updating by means of Kalman filtering. Template matching can deal even with total occlusions during a period of several frames. Nevertheless, when no prior model is available, the most common approach is statistical region classification, like Bayesian clustering (Chang et al., 1997; Montoliu and Pla, 2005). These techniques are very sensitive to noise and aliasing. Furthermore, they do not provide