Algorithm Research And System Design Of Human Motion Recognition Based On Kinect

There are many problems in the traditional human motion recognition algorithm.For example,human motion acquisition equipment is susceptible to external factors such as light intensity,human motion description is unclear,and human motion characteristics are susceptible to physical transformation.In view of the above problems,this paper studies to the human motion recognition algorithm from the aspects of data acquisition,feature extraction and classification identification to reduce the impact of the above problems.Therefore,this paper uses Kinect for extracting human joint data to overcome the illumination problem,selects CNN to extract advanced human motion features,and selects SoftMax to complete multi-category tasks.Based on this,the human motion detection system is developed.The main research contents are as follows:?1?For the traditional camera is susceptible to visual problems such as illumination and occlusion,using the bone data collected by Kinect as the data source.Kinect is capable of capturing depth images,and bone data is derived from depth images,thus overcomes the effects of illumination.Kinect can predict the position of the occlusion,greatly reduces the impact of occlusion on human motion recognition.?2?Aiming at the problems of manual extraction of human body motion features and poor feature expression,CNN-based human body motion recognition algorithm is designed.First,the normalization method suitable for this paper is implemented,that is,the data is first moved to the positive axis according to the coordinate axis direction,and then 0-1 normalized.Secondly,six kinds of data coding schemes are proposed.Six kinds of data coding schemes are six kinds of conjectures that drawing motion data into images.The optimal coding scheme is selected in Matlab simulation,and finally the grayscale image whirling coding scheme is selected.According to the actual needs,build a suitable CNN model,and finally achieve more than 90% recognition accuracy on the MSRC₁2 database,and have a good generalization ability.?3?Design human body motion detection system.The real-time detection is realized by adding a sliding window method,and the simulation is performed in Matlab,and the optimal sliding step length is 13.Using Kinect to collect human bone data in real time,realize the data acquisition module of human motion detection system.The CNN model was built using Caffe,and the MSRC₁2 database training model was used to realize the motion recognition module of the human motion detection system.Taking all factors into consideration,the data acquisition module and the motion detection module are combined to design a human motion detection system.Finally,the effectiveness of the human motion recognition system was verified by experiments.