Tracking human motion and actions for interactive robots
Tracking human motion and actions for interactive robots
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DOI：
10.1145/1228716.1228765
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发表时间：
2007-03
期刊：
影响因子：
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通讯作者：
O. C. Jenkins;Germán González Serrano;M. Loper
中科院分区：
文献类型：
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作者：
O. C. Jenkins;Germán González Serrano;M. Loper
提出了一种基于单目机器人视觉的运动学姿态估计和动作识别方法。我们提出了利用动态运动词汇对桥接的决策观察人类和机器人传感信息。我们的运动词汇由学习过的原语组成，这些原语构建了用于决策的动作空间，并描述了人类运动动态。给定随时间的图像观察，每个基元在粒子滤波器的上下文中使用其对运动动态的预测密度独立地推断姿势。对来自并行推理的一组基元的姿势估计进行仲裁，以估计正在执行的动作。我们的方法的有效性证明，通过跟踪和动作识别扩展运动试验。结果证明了该算法对未分割的多动作运动、运动速度和摄像机视点的鲁棒性。
A method is presented for kinematic pose estimation and action recognition from monocular robot vision through the use of dynamical human motion vocabularies. We propose the utilization of dynamical motion vocabularies towards bridging the decision making of observed humans and information from robot sensing. Our motion vocabulary is comprised of learned primitives that structure the action space for decision making and describe human movement dynamics. Given image observations over time, each primitive infers on pose independently using its prediction density on movement dynamics in the context of a particle filter. Pose estimates from a set of primitives inferencing in parallel are arbitrated to estimate the action being performed. The efficacy of our approach is demonstrated through tracking and action recognition over extended motion trials. Results evidence the robustness of the algorithm with respect to unsegmented multi-action movement, movement speed, and camera viewpoint.