基于视觉的运动检测与手部动作识别-模式识别与智能系统专业论文.docx

VI VI 和基于像素的背景模型相结合，构建了一个有效的层次背景模型。在 对于局部区域的建模中，本文还提出了一种新的局部区域描述符。该 描述符综合运用色彩不变量和纹理特征，对局部特征进行了更准确的 描述，对动态背景和光照变化等具有更好的适应性。 在准确提取前景的基础上，本文对手部的特定动作进行识别。手 部动作识别在人机交互、视频监控等方面有丰富的应用。本文的手部 动作识别算法分为三个主要部分：手部特征检测、基本动作分割和动 作轨迹识别。本文算法将三维的动作转化为二维的轨迹，并利用 Hausdorff距离来实现具有平移和缩放变换的轨迹识别。 关键词：运动检测，背景模型，颜色矩，码书，阴影去除，Hausdorff 距离，动作识别 VII VII Vision-based moving object detection and gesture recognition ABSTRACT Identifying moving objects from a video sequence is a fundamental and critical task in many computer-vision applications. A common approach is to perform background subtraction, which identifies moving objects from the portion of a video frame that differs significantly from a background model. There are many challenges in developing a good background subtraction algorithm. First, it must be robust against changes in illumination. Second, it should avoid detecting non-stationary background objects such as swinging leaves, rain, snow,and shadow cast by moving objects. Based on the study of existing methods, I propose several modified methods to overcome these problems. This paper combines HSV shadow suppression into the background model to deal with shadows, highlights and illumination changes. To deal with moving backgrounds, this paper propose a new background update strategy which include the foreground information in the model. we propose a method that combines pixel-based and block-based approaches into a single framework. We show that efficient hierarchical backgrounds can be built by considering that these two approaches are complementary VIII VIII to each other. In addition, a novel descriptor is proposed for block-based background modeling in the coarse level of the hierarchy. Quantitative evaluations show that the proposed hierarchical method can provide better results than existing single-level approaches. Based on the accurate detection of foreground moving people, in this thesis, we address the issue of detecting and recognizing hand gesture in video sequences. First