Real-time video object segmentation using HSV space

A new method is proposed for real-time video object segmentation ?om static background under shadows and varying lighting. The method firstly learns a statistical model and a type model for a background scene in HSV space. The latter one represents the reflection characteristic of object surface in background and helps decide whether hue or saturation component should be used to detect changes in the statistical model. In the stage of change detection, multi-criteria decision is taken in order to deal with various lightness conditions such as shadows. At last a speedup morphologic Jilter and a connected components labeling algorithm will refine segmentation results. The method makes full and better use of color information in images. The capability of accurate segmentation and real-time execution is demonstrated for several test and practical video sequences. fairly good performance. But because of improper use of hue and saturation components, it runs unsteadily. In this paper, we propose a new method of segmenting objects from a video sequence captured by a fixed camera. To handle the problems mentioned above, we make use of some more information besides RGB in color images such as material characteristics of background objects, and detect changes with multiple criteria adaptive to light-changing condition. This method is robust and can be used in practical video processing system such as video conference. This paper is organized as follows. Section2 introduces the statistical model and the type model of background in HSV space. Section3 presents the algorithm of change detection. Post processing is described in Section4. Experiment results are given in SectionS. The paper is summarized in Section6. 2. MODELING BACKGROUND 1; INTRODUCTION Efficient VO segmentation is a hndamental and crucial issue in video processing. Although the general segmentation is an ill-problem, we can explore real-time automatic segmentation algorithms for some specific applications, such as video surveillance and video conference. Video sequences in these applications are always captured by fixed cameras under poor environments and in need of robust object segmentation algorithms. A typical approach to extracting objects from static background scene is background subtraction [l] [3]. Its main idea is comparing current frame with a background image according to certain criteria. Elements different from the background image belong to foreground objects. A number of video object segmentation systems follow this way. However, they are sensitive to shadows or light changes. Thus segmenting objects in color spaces other than RGB draws more attention. Pfinder [2] detects changes in YUV color space, and Horprasert [ l ] uses a color model consisting of brightness distortion and chromaticity distortion for color images. 'Both of them overcome this problem to some extent but are not as robust as we expect. Francois [3] proposes to operate segmentation in HSV color space and achieves 0-7803-7622-6/02/$17.00 02002 IEEE I1 85 In this section, we present the advantage of HSV color space and detail the procedures of modeling background with a statistical model and a type model respectively. 2.1. HSV Color Space The traditional RGB color space is used in most color CRT monitors and color raster graphics. Though suitable for recording and transmitting color images, it is actually not intuitive because the discriminable color difference is non-linear. From this point of view, some color spaces, like HSV and HLS, resemble to human vision system and are convenient to color processing. There are two distinct advantages of this kind of color spaces: 1) the brightness or lightness component is irrelevant to chromatic information of images; 2) the chromatic component consisting of hue and saturation is intuitive. HSV space (HSV represents hue, saturation and value, respectively), appearing as a cylinder rather than a cubic, can be simply converted from RGB space and fit for real-time systems. 2.2. Statistical Model of Background We setup the statistical model of background with N frames of the static background. Because of white noise of