用于动作识别的人类手势分析

Kaveri V Sonani, M. Zaveri, Sanjay Garg
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引用次数: 4

摘要

在本文中,我们提出了一种人类手势分析算法,用于使用微软kinect传感器进行动作识别,以构建物理治疗应用。Kinect能够从RGB图像生成深度图像,并从深度图像生成人体骨骼。在这种方法中,治疗师可以记录运动,并要求患者在家中模仿该运动。该系统能够跟踪他们的进展,并识别患者的动作。如果患者表现不佳,则根据人体骨骼关节之间的角度信息给出建议。我们为每个动作设计了码本,其中包含每个动作不同的关键姿态帧。为了找到两帧之间的匹配,我们使用了星距的概念。我们用大量的场景来评估我们提出的系统,并使用隐马尔可夫模型进行分析以识别动作。
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Human gesture analysis for action recognition
In this paper, we propose an algorithm for human gesture analysis for the action recognition using Microsoft kinect sensor to build physiotherapy application. Kinect is able to generate depth image from RGB image and generate human skeleton from the depth image. In this method, therapist may record the exercise and patients are required to mimic that exercise at home. This system is able to track their progress as well as recognize the action of patients. If a patient do not perform properly, then it gives the suggestion based on the information of angle between joints of human skeleton. We design codebook for each action, which contains different key posture frames for each action. To find the match between two frames, we make the use of the concept of star distance. We evaluate our proposed system with large number of scenarios and analysed with Hidden Markov Model to recognise the action.
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