• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Computer Graphics Theory and Applications >Towards a Fully Automatic Markerless Motion Analysis System for the Estimation of Body Joint Kinematics with Application to Sport Analysis
【24h】

Towards a Fully Automatic Markerless Motion Analysis System for the Estimation of Body Joint Kinematics with Application to Sport Analysis

机译：朝着全自动无标记运动分析系统，用于估计体育分析的体育 - 分析

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

We present a complete markerless motion analysis system for the estimation of human body joint kinematics (JK) in sports. The system works in two phases, an offline phase in which a reference 3D scan (model) of the athlete is acquired. A skeleton consisting of a number of articulated joints is then fitted to the model based on anatomical data. In the online phase, a number of video cameras are utilized to capture the athlete motion from different viewpoints. In order to reconstruct the 3D motion of the athlete, a method is developed to estimate the intrinsic and the extrinsic parameters of all cameras. Synchronized videos are then captured of the athlete while performing an action then a kernel density estimator based background segmentation algorithm is applied to extract the silhouettes of the athlete in each video which are then reprojected to their 3D source to reconstruct an estimate of the body shape (visual hull). To estimate the JK, constrained optimization techniques are then used to extract information from the shape and the skeleton of the reconstructed VH. This includes a number of geometrically-driven cost functions driven by mathematical and physical constraints of the human body to align and deform the reference 3D model to the shape of the VH. Unlike existing pose estimation approaches where a reference pose with labeled body parts are used to initialize the pose estimation process, our approach identifies the the body shape and parts automatically and without any assumptions of its temporal continuity when dealing with unrelated frames which is a very challenging task due to the high dimensionality of the pose space. The proposed system modules are tested with real examples and achieved a promising results.
机译：我们为体育中的人体关节运动学（JK）估算了一个完整的无标记运动分析系统。系统工作在两个阶段，其中获取了运动员的参考3D扫描（型号）的离线阶段。基于解剖数据，然后将由多个铰接性接头组成的骨架，然后根据解剖数据。在在线阶段，利用许多视频摄像头从不同的视点捕获运动员运动。为了重建运动员的3D运动，开发了一种方法来估计所有相机的内在和外在参数。然后在执行动作的同时捕获运动员的同步视频，然后应用基于内核密度估计器的背景分割算法，以提取在每个视频中的运动员的轮廓，然后将其恢复到其3D源以重建体形的估计（视觉船体）。为了估计JK，然后使用受约束的优化技术来从重建VH的形状和骨架中提取信息。这包括由人体的数学和物理约束驱动的许多几何驱动的成本函数，以将参考3D模型对准和使参考3D模型变形为VH的形状。与现有的姿势估计方法不同，其中使用标记的身体部位的参考姿势才能初始化姿势估计处理，我们的方法自动识别身体形状和部件，并且在处理不相关的框架时，没有任何假设是一个非常具有挑战性的由于姿势空间的高度，任务。建议的系统模块与实际示例进行测试，并实现了有希望的结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号