| dc.description.abstract | The objective of this study is to recognize human’s five postures which are captured from the set of Kinect. By using horizontal projection, star skeleton, neural network, and similar feature process techniques, five human’s postures, which contain standing, sitting, bending, keeling and lying, are recognized. After Kinect captures the picture of a human, a silhouette contour of the human is segmented from the background based on the difference of depth data between the human’s body and background. Then the horizontal projection is utilized to identify whether the posture is keeling or not. If it is not a kneeling posture, a star skeleton is used to calculate five maximum distances from the feature points to the centroid of the human body. The five branches of the star skeleton and depth data are the inputs to train the network of Learning Vector Quantization (LVQ). Subsequently, the outputs of the LVQ are utilized to recognize the five postures including standing, forward sitting, non-forward sitting, bending, and lying. The standing and non-forward sitting postures are processed by the similar feature process based on the horizontal and vertical projection. The hand-shaking disturbance is filtered to calculate the length and breadth ratio of human so as to improve the ratio of posture recognition. The posture recognition system can not only be applied to different indoor environments and different distances between Kinect and human, but also achieve the goal of real-time and stable posture recognition for different human physiques. Therefore, the system can be practically applied to home nursing and amusement place care.
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