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首页> 外文会议>International Conference on Computational Science and Its Applications(ICCSA 2004) pt.4; 20040514-20040517; Assisi; IT >Model-Based Human Motion Tracking and Behavior Recognition Using Hierarchical Finite State Automata
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Model-Based Human Motion Tracking and Behavior Recognition Using Hierarchical Finite State Automata

机译：分层有限状态自动机的基于模型的人体运动跟踪与行为识别

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摘要

The generation of motion of an articulated body for computer animation is an expensive and time-consuming task. Recognition of human actions and interactions is important to video annotation, automated surveillance, and content-based video retrieval. This paper presents a new model-based human-intervention-free approach to articulated body motion tracking and recognition of human interaction using static-background monocular video sequences. This paper presents two major applications based on basic motion tracking: motion capture and human behavior recognition. To determine a human body configuration in a scene, a 3D human body model is postulated and projected on a 2D projection plane to overlap with the foreground image silhouette. We convert the human model body overlapping problem into a parameter optimization problem to avoid the kinematic singularity problem. Unlike other methods, our body tracking does not need any user intervention. A cost function is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The configuration the best overlap with the foreground of the image least overlap with the background is sought. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the 2D projection plane domain. We recognize human interaction motion using hierarchical finite state automata (FA). The model motion data we get from tracking is analyzed to get various states and events in terms of feet, torso, and hands by a low-level behavior recognition model. The recognition model represents human behaviors as sequences of states that classify the configuration of individual body parts in space and time. To overcome the exponential growth of the number of states that usually occurs in a single-level FA, we present a new hierarchical FA that abstracts states and events from motion data at three levels: the low-level FA analyzes body parts only, the middle-level FAs recognize motion and the high-level FAs analyze a human interaction. Motion tracking results and behavior recognition from video sequences are very encouraging.
机译：用于计算机动画的关节运动的产生是一项昂贵且费时的任务。识别人类行为和互动对于视频注释，自动监视和基于内容的视频检索非常重要。本文提出了一种新的基于模型的无人干预方法，用于使用静态背景单眼视频序列进行关节运动跟踪和人机交互识别。本文介绍了基于基本运动跟踪的两个主要应用：运动捕获和人类行为识别。为了确定场景中的人体配置，假定3D人体模型并将其投影在2D投影平面上以与前景图像轮廓重叠。我们将人体模型主体重叠问题转换为参数优化问题，以避免运动学奇点问题。与其他方法不同，我们的身体跟踪不需要任何用户干预。成本函数用于估计前景输入图像轮廓和投影的3D模型人体轮廓之间的重叠程度。寻找与图像的前景重叠最好的与背景重叠最少的配置。通过将一组像素从图像域转换为2D投影平面域中的多边形，使用计算几何来计算重叠。我们使用分层有限状态自动机（FA）来识别人类交互运动。我们通过低级行为识别模型分析了从跟踪中获得的模型运动数据，以获取有关脚，躯干和手的各种状态和事件。识别模型将人类行为表示为状态序列，该状态序列将各个身体部位在空间和时间上的配置分类。为了克服通常在单级FA中发生的状态数量的指数增长，我们提出了一种新的分层FA，该FA从三个级别的运动数据中抽象出状态和事件：低级FA仅分析身体部位，中级FA级别的FA识别运动，而高层的FA则分析人与人之间的互动。视频序列的运动跟踪结果和行为识别非常令人鼓舞。

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