@article{oai:shonan-it.repo.nii.ac.jp:02000097,
 author = {熊谷, 兼太郎 and 小野, 憲司 and KUMAGAI, Kentaro and ONO, Kenji},
 issue = {1},
 journal = {湘南工科大学紀要, SHONAN INSTITUTE OF TECHNOLOGY JOURNAL},
 month = {Mar},
 note = {公共施設の建物の入口などに設置したセンサで来訪者の行動を測定し，離れた場所からもリアルタイムでその情報を取得できるようなシステムを提案するとともに，機械学習の手法を用いて，毎日の建物入場者の人流を特徴に基づいて2群程度に分類し，未知の日についても類似度によってそのどちらかの群にリアルタイムに仕分けたうえで，低い確率でしか発生しない異常な事象を検知する実用的な手法を構築した．

In this study, a practical system which consists of infrared sensors and computer programs for machine learning was proposed for anomaly detection of pedestrian flow. Utilizing time-series data collected by the sensors at an entrance of a building in a university as training data for machine learning, the proposed system successfully classified the data into two groups, based on characteristics of flow of visitors to the building. Subsequently, the system was applied to data set which is collected in real-time, and it was shown that the system can detect properly abnormal events that occur only with low probability.},
 pages = {37--47},
 title = {センサで取得した人流情報に基づく異常検知システム},
 volume = {56},
 year = {2022},
 yomi = {クマガイ, ケンタロウ and オノ, ケンジ}
}