부산대학교 도서관

The 3D Convolutional Neural Network (CNN) is used for Kinesis Recognition in this paper. The suggested model exhibits enhanced recognition accuracy of intricate hand and body movements by acquiring spatiotemporal information from kinesis sequences. The network can more reliably recognize complex motions because it can simultaneously utilize spatial and temporal information. By means of comprehensive testing and assessment, this work demonstrates the efficacy of the 3D CNN architecture in real-time kinesis categorization, indicating that it is a viable option for a variety of uses, including virtual reality, sign language interpretation, and human-computer interaction.