{"product_id":"ieej-btc205ss010007","title":"Temporal Action Segmentation of Ultra-Low Frame Rate Excavator Work Video at the Construction Site Using Transformer-Based Model (ASFormer)","description":"\u003cp\u003e\u003cstrong\u003eカテゴリ：\u003c\/strong\u003e部門大会\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e論文No：\u003c\/strong\u003eSS1-7\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eグループ名：\u003c\/strong\u003e【C】2025年電気学会電子・情報・システム部門大会\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e発行日：\u003c\/strong\u003e2025\/8\/20\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eタイトル(英語)：\u003c\/strong\u003eTemporal Action Segmentation of Ultra-Low Frame Rate Excavator Work Video at the Construction Site Using Transformer-Based Model (ASFormer)\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e著者名：\u003c\/strong\u003eSereepookkana Kanok（）,Orachon Teerapong（KOSEN-KMITL, King Mongkut's Institute of Technology Ladkrabang, Thailand）,Doi Shigeo（苫小牧工業高等専門学校）,Itayama Yuichiro（株式会社 EARTHBRAIN）\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e著者名(英語)： \u003c\/strong\u003eKanok Sereepookkana (),Teerapong Orachon (King Mongkut's Institute of Technology Ladkrabang),Shigeo Doi (National Institute of Technology, Tomakomai College),Yuichiro Itayama (EARTHBRAIN)\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eキーワード：\u003c\/strong\u003eTemporal Action Segmentation,Ultra-Low Frame Rate Video,Excavator Work Video,Egocentric Video,Transformer-Based ModelASFormer\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e要約(日本語)：\u003c\/strong\u003eConstruction sites leverage technology to track on-site situations, enabling more efficient planning of construction projects. One popular method for tracking is by installing a camera on excavator machines to record construction work videos, as this approach is resource-efficient. These raw videos, referred to as untrimmed videos, are valuable because they can be analyzed to gain insights into on-site activities. To automatically analyze and convert untrimmed videos into segmented videos, the Temporal Action Segmentation (TAS) technique is employed. Our work focuses on a successive frame-level classification Transformer-based TAS model, ASFormer. Experimental results show that ASFormer can also be effectively applied to our excavator dataset, achieving a frame-wise accuracy of 83.13%.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e本誌掲載ページ：\u003c\/strong\u003e1777-1779p\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e原稿種別：\u003c\/strong\u003e英語\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePDFファイルサイズ：\u003c\/strong\u003e301Kバイト\u003c\/p\u003e","brand":"IEEJ-PDF","offers":[{"title":"PDFダウンロード（一般価格440円\/会員価格220円） \/ A4 \/ 2","offer_id":47942184239343,"sku":"IEEJ-BTC205SS010007-PDF","price":440.0,"currency_code":"JPY","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0718\/9512\/2159\/files\/IEEJ-PDF_bumontaikai_a9197e6c-7a17-4346-979b-03e72a2be28a.png?v=1773064627","url":"https:\/\/ieej.bookpark.ne.jp\/products\/ieej-btc205ss010007","provider":"電気学会 電子図書館","version":"1.0","type":"link"}