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Human extremity detection and its applications in action detection and recognition.

机译：人体肢体检测及其在动作检测和识别中的应用。

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摘要

It is proven that locations of internal body joints are sufficient visual cues to characterize human motion. In this dissertation I propose that locations of human extremities including heads, hands and feet provide powerful approximation to internal body motion.I propose detection of precise extremities from contours obtained from image segmentation or contour tracking. Junctions of medial axis of contours are selected as stars. Contour points with a local maximum distance to various stars are chosen as candidate extremities. All the candidates are filtered by cues including proximity to other candidates, visibility to stars and robustness to noise smoothing parameters.I present my applications of using precise extremities for fast human action detection and recognition. Environment specific features are built from precise extremities and feed into a block based Hidden Markov Model to decode the fence climbing action from continuous videos. Precise extremities are grouped into stable contacts if the same extremity does not move for a certain duration. Such stable contacts are utilized to decompose a long continuous video into shorter pieces. Each piece is associated with certain motion features to form primitive motion units. In this way the sequence is abstracted into more meaningful segments and a searching strategy is used to detect the fence climbing action. Moreover, I propose the histogram of extremities as a general posture descriptor. It is tested in a Hidden Markov Model based framework for action recognition.I further propose detection of probable extremities from raw images without any segmentation. Modeling the extremity as an image patch instead of a single point on the contour helps overcome the segmentation difficulty and increase the detection robustness. I represent the extremity patches with Histograms of Oriented Gradients. The detection is achieved by window based image scanning. In order to reduce computation load, I adopt the integral histograms technique without sacrificing accuracy. The result is a probability map where each pixel denotes probability of the patch forming the specific class of extremities. With a probable extremity map, I propose the histogram of probable extremities as another general posture descriptor. It is tested on several data sets and the results are compared with that of precise extremities to show the superiority of probable extremities.
机译：事实证明，人体内部关节的位置足以表征人的运动。在这篇论文中，我提出人体四肢的位置，包括头部，手和脚，为人体内部运动提供有力的近似方法。我提议从图像分割或轮廓跟踪获得的轮廓中检测出精确的肢体。轮廓中轴的交点被选为星形。选择与各种恒星具有局部最大距离的轮廓点作为候选末端。所有候选对象均经过提示过滤，包括与其他候选对象的接近程度，对星星的可见性以及对噪声平滑参数的鲁棒性。我介绍了使用精确四肢进行快速人类动作检测和识别的应用。特定于环境的功能是从精确的肢体构建的，并馈入基于块的“隐马尔可夫模型”，以从连续视频中解码围栏爬升动作。如果同一肢体在一定时间内没有移动，则将这些肢体分为稳定的接触。利用这种稳定的触点将长的连续视频分解成较短的片段。每一块都与某些运动特征相关联以形成原始运动单元。这样，序列被抽象为更有意义的部分，并且使用搜索策略来检测围栏攀爬动作。此外，我提出了四肢的直方图作为一般的姿态描述符。它在基于隐马尔可夫模型的动作识别框架中进行了测试。我进一步建议在不进行任何分割的情况下，从原始图像中检测出可能的肢体。将末端建模为图像补丁而不是轮廓上的单个点有助于克服分割困难并提高检测的鲁棒性。我用“定向梯度直方图”表示四肢斑块。通过基于窗口的图像扫描来实现检测。为了减少计算量，我在不牺牲准确性的情况下采用了积分直方图技术。结果是一个概率图，其中每个像素表示面片形成特定肢体类别的概率。借助一个可能的肢体贴图，我提出了可能的肢体直方图作为另一个一般姿势描述符。它在多个数据集上进行了测试，并将结果与​​精确肢体的结果进行比较，以显示可能肢体的优越性。

著录项

  • 作者

    Yu, Qingfeng.;

  • 作者单位

    The University of Texas at Austin.;

  • 授予单位 The University of Texas at Austin.;
  • 学科 Engineering Computer.Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 130 p.
  • 总页数 130
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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