Wavelet-based Moving Object Segmentation From Scalar Wavelets to Dual-tree Complex Filter Banks

In this chapter we explain wavelet based moving object detection and segmentation in video frames. Starting from discrete wavelet transform (DWT), we show the recent developments employing multi-wavelets (MW), and later on we switch to the use of dual-tree complex wavelet transform (DT-CWT). Working on a video instead of an image requires more attention, and at the same time, proposes novel approaches such as the so-called non-separable 3D oriented dual-tree complex wavelet transform. A comprehensive comparison shows the advantages and disadvantages of current wavelet based techniques used in image/video segmentation. The segmentation and tracking of moving objects in video are important tasks in many applications. These tasks make it possible for video coding standards such asMPEG4 Sikora (1997), which provides content based functionalities through the concept of video object plane while employing ideas like content based scalability, as well as separate and flexible reconstruction and manipulation of contents Kim &Hwang (2002). The video surveillance systems designed for security applications need to track and furthermore distinguish intruding objects. All these applications require algorithms to detect, segment and track moving objects so that further high level processing can be performed. The approaches for moving object segmentation can be categorized into several groups. In fact, video segmentation algorithms can be categorized into four general subgroups; segmentation based on motion information only, segmentation based on motion and spatial information, segmentation based on change detection, and segmentation based on edge detection Kim & Hwang (2002). Due to the lack of spatial information motion segmentation techniques, which are closely related to motion estimation, suffer from occlusion and aperture problem. This limits the accuracy of the boundaries of segmented objects. The algorithms in the second group are suggested to improve some of the weak points of the first group. Spatial information is blended with motion information to make algorithms more stable in extraction of object boundaries. However, these techniques are not suitable for content based applications as they are not necessarily characterized by similar intensity, color, or motion. The algorithms in the third group start with the gray value difference image between two consecutive frames, and then a decision rule is applied on the absolute difference in order to identify moving areas. If the moving objects are not sufficiently textured, only the occlusion areas are marked as changed and interior of the objects remain unchanged. Therefore, the objects that stop moving for a certain period of time will be lost. The algorithms in the last category address video sequences as the 8