@techreport{oai:ipsj.ixsq.nii.ac.jp:00224211,
 author = {渡邊, 淳平 and 新熊, 亮一 and ガブリエレ, トロヴァト and Junpei, Watanabe and Ryoichi, Shinkuma and Gabriele, Trovato},
 issue = {19},
 month = {Feb},
 note = {This report presents an edge computing system that can classify people’s postures in an office using a Light Detection and Ranging (LIDAR) sensor network. The proposed system uses a deep learning model on point data for classification. Several classification models are generated by combining the location and number of LIDARs, feature types, and clustering methods. Each model performs classification for the test data. We evaluate the accuracy of the classification., This report presents an edge computing system that can classify people’s postures in an office using a Light Detection and Ranging (LIDAR) sensor network. The proposed system uses a deep learning model on point data for classification. Several classification models are generated by combining the location and number of LIDARs, feature types, and clustering methods. Each model performs classification for the test data. We evaluate the accuracy of the classification.},
 title = {オフィス環境における3 次元点群データからの人の作業状態の推論},
 year = {2023}
}