Abstract
With the continuous emergence of depth image recognition technology, human motion recognition technology has gradually come to life. However, because the current technology of image recognition and skeletal tracking is too backward, people cannot use gesture recognition robots to perform gesture recognition and correction with high accuracy for athletes. In this study, the skeletal tracking depth image is obtained through the Kinect sensor. When using the Kinect sensor to acquire images, the requirements for the environment around the measured object are very low, and it will not be affected by conditions such as light, shadow, and object occlusion, and the pose can be segmented in real time in a complex background. In this study, the depth map feature and the bone information feature are selected for fusion. The HOD feature is difficult to deal with the occlusion problem, and it is not easy to detect the excessive range of the human body gesture or the change of the object direction. Based on this, the HOD feature is improved in many places to form a new 3D-HOD feature and DMM-HOG feature. In this study, the technical research on posture recognition and correction of swimmers in the experiment has realized posture collection, posture segmentation, posture analysis, posture modeling, and posture recognition. In this study, random forests are combined with HOD features to classify pixel-by-pixel points in depth images. The accuracy of training pixel classification is as high as 85%, and the average recognition time of the decision tree for each pose is about 9% higher than that of random forests. The use of Meanshift algorithm to cluster the classified pixels to form skeletal joint points is fast and efficient, and can quickly and accurately find joint points. It has also achieved good experimental effects on human motion recognition and continuous motion recognition, is more suitable for real life, and has commercial practical value.
Highlights
As a natural interaction method, gesture recognition will be widely used in robot control
The skeletal tracking depth image is obtained through the Kinect sensor, and the pose can be segmented in real time in a complex background
Random forests are combined with HOD features to classify pixel-by-pixel points in depth images. e accuracy of training pixel classification is as high as 85%, and the average recognition time of the decision tree for each pose is about 9% higher than that of random forests. is study has achieved good experimental results on human motion recognition and continuous motion recognition, and is more suitable for robots to determine the swimmer’s posture
Summary
As a natural interaction method, gesture recognition will be widely used in robot control. In reference [3], the author proposed an ultrasonic gesture recognition method based on context-aware information. In reference [16], the author proposes an online control programming algorithm for human-computer interaction systems, in which robot movements are controlled by the operator’s gesture recognition results based on visual images [17]. In reference [24], the author proposes a skin parameter optimization method based on depth image sequences. In references [27, 28], this study proposes an effective salient object segmentation method based on depth perception image layering. In references [35, 36], the author proposes a novel method that uses a convolutional neural network to enable a mobile robot to estimate its rough position in a 3D map using only a monocular camera. Random forests are combined with HOD features to classify pixel-by-pixel points in depth images. e accuracy of training pixel classification is as high as 85%, and the average recognition time of the decision tree for each pose is about 9% higher than that of random forests. is study has achieved good experimental results on human motion recognition and continuous motion recognition, and is more suitable for robots to determine the swimmer’s posture
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