特征融合下田径录像3D人体动作DTW捕捉算法OA北大核心CSTPCD

Due to the varying movement styles and speeds among athletes,track and field videos may have sequences of differing lengths,causing misalignment.Thus,a feature fusion based Dynamic Time Warping(DTW)algorithm for capturing 3D human body movements in track and field videos is proposed.Human motion data from the track and field videos are converted into 3D coordinates,which represent body positions and movements.A depth map sequence is then obtained,and Gradient Local Anisotropy Coefficient(GLAC)and Sparse Time Auto Correlation of Gradients(STACOG)are used to analyze gradient characteristics and temporal autocorrelation of local areas in the depth map.Wavelet transform and k-means clustering are combined to analyze the dynamic changes in the human body contours using the Canny operator,which extracts edge contours of each depth map frame.Principal Component Analysis(PCA)method is used to fuse multiple features,such as 3D spatial coordinates and joint angles,into one feature space by employing Kinect devices,which extracts 3D coordinate information of human skeletal points.Preprocessing is carried out through frame filling and deletion operations,and the most important principal components are selected to construct a new low dimensional feature space.The DTW algorithm is used to calculate the similarity in the video sequences and capture 3D human movements in sequences of differing lengths in track and field recordings.The experimental results show that the accuracy of the algorithm in capturing 3D human movements in track and field videos reaches 99.07%.When faced with complex actions,the similarity between the human actions captured by this algorithm and actual actions remains above 97%.The human action contour feature lines extracted by this algorithm are smooth,continuous,and highly consistent with the actual action.