Abstract
Motion pose capture technology can effectively solve the problem of difficulty in defining character motion in the process of 3D animation production and greatly reduce the workload of character motion control, thereby improving the efficiency of animation development and the fidelity of character motion. Motion gesture capture technology is widely used in virtual reality systems, virtual training grounds, and real-time tracking of the motion trajectories of general objects. This paper proposes an attitude estimation algorithm adapted to be embedded. The previous centralized Kalman filter is divided into two-step Kalman filtering. According to the different characteristics of the sensors, they are processed separately to isolate the cross-influence between sensors. An adaptive adjustment method based on fuzzy logic is proposed. The acceleration, angular velocity, and geomagnetic field strength of the environment are used as the input of fuzzy logic to judge the motion state of the carrier and then adjust the covariance matrix of the filter. The adaptive adjustment of the sensor is converted to the recognition of the motion state. For the study of human motion posture capture, this paper designs a verification experiment based on the existing robotic arm in the laboratory. The experiment shows that the studied motion posture capture method has better performance. The human body motion gesture is designed for capturing experiments, and the capture results show that the obtained pose angle information can better restore the human body motion. A visual model of human motion posture capture was established, and after comparing and analyzing with the real situation, it was found that the simulation approach reproduced the motion process of human motion well. For the research of human motion recognition, this paper designs a two-classification model and human daily behaviors for experiments. Experiments show that the accuracy of the two-category human motion gesture capture and recognition has achieved good results. The experimental effect of SVC on the recognition of two classifications is excellent. In the case of using all optimization algorithms, the accuracy rate is higher than 90%, and the final recognition accuracy rate is also higher than 90%. In terms of recognition time, the time required for human motion gesture capture and recognition is less than 2 s.
Highlights
Virtual reality (VR) is a virtual immersive interactive environment that uses modern high-tech with computer technology as the core to generate a specific range of realistic visual, auditory, and tactile virtual environments [1]. e research purpose of VR technology is to create such a simulated virtual environment so that users can interact with objects in the environment with necessary equipment to achieve an “immersive” effect, just like the feeling and experience of the real environment [2]
Experimental results show that no matter what optimization method is used, the recognition accuracy of the two classifications has reached more than 90%; the accuracy of the grid search algorithm grid search-support vector classifier (GS-SVC) in the multiclassification mode is not ideal, and the error rate is about 10%; the recognition accuracy of genetic algorithm-support vector classifier (GA-SVC) and particle swarm optimization-support vector classifier (PSO-SVC) of the heuristic algorithm reached more than 92% in the twoclassification mode
According to the angular velocity, motion acceleration, and geomagnetic field strength of each axis, this paper proposes an adaptive adjustment method based on the motion state. e output of the sensor and the estimated motion acceleration of each axis are used as the judgment basis of fuzzy logic to identify the current carrier motion
Summary
Virtual reality (VR) is a virtual immersive interactive environment that uses modern high-tech with computer technology as the core to generate a specific range of realistic visual, auditory, and tactile virtual environments [1]. e research purpose of VR technology is to create such a simulated virtual environment so that users can interact with objects in the environment with necessary equipment to achieve an “immersive” effect, just like the feeling and experience of the real environment [2]. With the rapid development of computer technology, the New York Computer Graphics Technology Laboratory has designed an mercury mirror optical device to project the performance poses of real dancers on the computer screen as a reference for the key frames of digital dancer animation [5, 6] It promotes the development of motion gesture capture technology. In order to further improve the estimation accuracy and system stability, the information distribution factor of the filter and the noise matrices R and Q are adjusted in real time, and an adaptive adjustment method based on fuzzy logic is adopted to enable it to be uniform in various motion states It can ensure the stability of the output, further simplify the fault detection and isolation algorithm, and reduce the burden of fault-tolerant calculations.
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