{"doi":"10.3156/jsoft.29.1_517","title":"Video-based Estimation System Using Convolutional Neural Networks for Audiences’ State in the Classroom and Discussion of its Essential Image Features","abstract":null,"journal":"Journal of Japan Society for Fuzzy Theory and Intelligent Informatics","year":2017,"id":602797,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1546123,"name":"Hitoshi IYATOMI","orcid":null,"position":1,"is_corresponding":false},{"id":1546122,"name":"Daiki SHIMADA","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Video-based Estimation System Using Convolutional Neural Networks for Audiences’ State in the Classroom and Discussion of its Essential Image Features","abstract":"高等教育の質に対する社会的関心は高く，faculty development（FD）という授業改善活動が広く行われている．FDのための教員への授業のフィードバック手段として，聴講者の受講状態を解析する工学的アプローチが存在する．カメラによって撮影された映像から聴講者の状態を解析する手法は，コストや聴講者への影響の面から，教育現場への導入可能性が高い．しかしこれまでの映像解析の試みでは，状況の多様性に対応できる効果的な特徴量が明らかでなく，またそれらの設計が十分でなかった. そのため，利用できる場面は限られていた．本研究では，聴講者の状態推定課題をパターン認識系と解釈し，機械学習の枠組みで特徴量獲得を行うconvolutional neural networks（CNN）を用いた聴講者の状態推定システムを提案する．そして，CNNを識別器として用いる際に抱える問題を取り上げ，提案システムが学習により獲得した特徴量について議論を行う．様々な環境で撮影された映像データについて性能評価実験を行った結果，提案システムは聴講者の検出性能で適合率が84.8%，再現率が61.8%，全状態の平均正答率で72.8%を達成した．CNNによる聴講者の状態推定は，学習データ数を制限した問題設定においても高い性能を示し，聴講者に関わる特徴を効率的に獲得することが示唆された．","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"42517934","pmcid":null,"openalex_id":"https://openalex.org/W2590798552","authors":[],"funders":[],"total_grants":0,"fwci":0.0902,"citation_percentile":0.41855448,"influential_citations":0,"citation_trend":[{"year":2019,"count":1}],"oa_status":"gold","license":null,"oa_locations":[{"url":"https://www.jstage.jst.go.jp/article/jsoft/29/1/29_517/_pdf","host_type":"journal"},{"url":"https://www.jstage.jst.go.jp/article/jsoft/29/1/29_517/_pdf","host_type":"publisher"},{"url":"https://doi.org/10.3156/jsoft.29.1_517","host_type":"journal"}],"fields_of_study":["Video Analysis and Summarization","Advanced Vision and Imaging"],"mesh_terms":[],"keywords":["Convolutional neural network","Computer science","State (computer science)","Artificial intelligence","Estimation","Image (mathematics)","Cellular neural network","Artificial neural network","Computer vision","Pattern recognition (psychology)","Algorithm","Engineering","Systems engineering"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-29T20:20:16.197080Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}