Objective Human action recognition based on 3D skeleton has been a popular topic in computer vision,the goal of which is to automatically segment,capture,and recognize human action.Human action recognition has been widely applied in real-world applications.For the past several decades,it has been used in surveillance,video games,robotics,human-human interaction,human-computer interaction,and health care,and has been widely explored by researchers since the 1960s.This study obtains 3D data in four ways.First,a motion capture system is used based on a marker.Second,multiple views are used for 2D image sequence reconstruction of 3D information.Third,range sensors are used.Fourth,RGB videos are used.However,extracting data by using a motion capture system and reconstruction is inconvenient.Range sensors are expensive and difficult to use in a human environment,and they obtain data slowly and provide a poorly estimated distance.Moreover,RGB images usually provide the appearance information of the objects in the scene.Given the limited information provided by RGB images,solving certain problems,such as the partition of the foreground and background with similar colors and textures,is difficult,if not impossible.Moreover,RGB data are highly sensitive to various factors,such as illumination,viewpoint,occlusions,clutter,or diversity of datasets.RGB video sensor data cannot capture the information that human needs.The rapid development of depth sensors,such as 3D Microsoft Kinect sensor,in recent years has provided not only color image data but also 3D depth image information.Three-dimensional depth images record the distance between object and body,thereby producing considerable information.Real-time skeletal-tracking technique and support vector machine recognize various postures and extract key information.The investigation of computer vision algorithms based on 3D skeleton algorithms has thus attracted significant attention in the last few years.Many researchers have been studying skeleton-based algorithms,which have presented numerous achievements and contributions.The present action recognition algorithm selects a fixed joint as the coordinate center,which leads to a low recognition rate.An adaptive skeleton center algorithm for human action recognition is proposed to solve the problem of low accuracy.Method In the algorithm,frames of skeleton action sequences are loaded onto a human action dataset,redundant frames are removed from the sequence frame information,and the original coordinate matrix is obtained by preprocessing the sequences.Rigid vector and joint angle features are generated by extracting the original coordinate matrix.The adaptive value can be determined on the basis of changes in rigid vector and joint angle values.The coordinate center can be adaptively selected according to the adaptive value and used to renormalize the original matrix.The action coordinate matrix is denoised by using a dynamic time-planning method.The Fourier time pyramid method is used to reduce the time displacement and noise problems of the action coordinate matrix.The matrix is classified by using support vector machine.Result Unlike existing algorithms,such as histogram of 3D joint (HO3DJ),conditional random field (CRF),EigenJoints,profile hidden Markov model (HMM),relation matrix of 3D rigid bodies + principal geodesic distance,and actionlet algorithms,the proposed algorithm exhibits improved performances on different datasets.On the UTKinect dataset,the action recognition rate of the proposed algorithm is 4.28％ higher than that of the HO3DJ algorithm and 3.48％ higher than that of the CRF algorithm.On the MSRAction3D dataset,the action recognition rate of the proposed algorithm is 9.57％ higher than that of the HO3DJ algorithm,2.07％ higher than that of the profile HMM algorithm,and 6.17％ higher than that of the EigenJoints algorithm.Action Set (AS) 1,AS2,and AS3 are subsets of the MSRAction3D dataset.The action recognition rate of the proposed algorithm is not as good as that of the other algorithms on the AS2 dataset,but the action recognition rates of the proposed algorithm are high on the AS1 and AS3 datasets.Conclusion The proposed algorithm solves the low accuracy problem of the existing action recognition algorithm.The coordinate center of a fixed joint is adopted.Simulation results show that the proposed algorithm can effectively improve the accuracy of human action recognition,and its action recognition rate is higher than those of existing algorithms.On the UTKinect dataset,the recognition rate of the proposed algorithm is at least 3％ higher than those of other algorithms,and the generated single-action recognition rate is as high as 90％.On the MSRAction3D dataset,the proposed algorithm shows advantages on AS1 and AS2 datasets,but its recognition rate on AS2 is not ideal,particularly in the recognition of the upper limb.Therefore,this algorithm needs improvement.The algorithm is generally efficient for single-action recognition.The next research direction is complex action recognition.
human action recognitionskeleton sequencefeature extractionadaptiverenormalize
NETL
NSTL
维普
万方数据
