Multi-modal human motion recognition based on behaviour tree Online publication date: Tue, 30-Apr-2024
by Qin Yang; Zhenhua Zhou
International Journal of Biometrics (IJBM), Vol. 16, No. 3/4, 2024
Abstract: Since the efficiency and accuracy of existing methods are low in complex multi-modal human motion recognition, this paper studies the multi-modal human motion recognition method based on behaviour tree. Firstly, Kinect sensor is used to collect multi-modal motion data of human body, and convolutional neural network is used to denoise the collected motion data. On the basis of denoising data, wavelet packet decomposition is used to extract its features. Finally, according to the extracted multi-modal human motion features, a behaviour tree model is constructed to traverse the recognised human motion and achieve accurate and efficient multi-modal human motion recognition according to the degree of feature matching. The experimental results show that the recognition accuracy of the proposed method can reach 98%, the highest recall rate is 96%, the highest F1 is 0.97, and the longest recognition time is only 4.65 seconds, which indicates that the proposed method has high practicability.
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