Preliminary Investigation of Packing Process Recognition Using Cross Knowledge Distillation

• Item type: SIG Technical Reports(1)
• 公開日: 2024-09-19
• タイトル: Preliminary Investigation of Packing Process Recognition Using Cross Knowledge Distillation
• タイトル / 言語: en
• タイトル: Preliminary Investigation of Packing Process Recognition Using Cross Knowledge Distillation
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 機械学習・知識蒸留手法
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者名: Hongyin, Qiao; Hamada, Rizk; Takuya, Maekawa
• 著者名(英): Hongyin, Qiao; Hamada, Rizk; Takuya, Maekawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Human activity recognition (HAR) often utilizes inertial measurement unit (IMU) data due to its effectiveness in various environments. However, IMU data lacks spatial position information and environmental context, limiting its robustness against changes in motion patterns, such as speed variations. Skeleton data, while providing detailed spatial information and better capturing human motion dynamics, can lead to serious occlusion problems in industrial environments with complex surroundings. To address these challenges, we propose a novel cross-knowledge distillation method, a machine-learning technique that transfers knowledge from a larger teacher model to a smaller student model. This approach enhances a student model based on IMU data through cross-modal knowledge distillation from a skeleton-based model. By leveraging the skeleton-based model to extract insights on capturing spatial and temporal information on the body in time-series data and superior prediction capabilities, the student model is significantly improved in its ability to capture intricate and rapidly evolving motion patterns. This method enables the student model to assimilate salient features from the teacher model, thereby enhancing overall performance in HAR tasks.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Human activity recognition (HAR) often utilizes inertial measurement unit (IMU) data due to its effectiveness in various environments. However, IMU data lacks spatial position information and environmental context, limiting its robustness against changes in motion patterns, such as speed variations. Skeleton data, while providing detailed spatial information and better capturing human motion dynamics, can lead to serious occlusion problems in industrial environments with complex surroundings. To address these challenges, we propose a novel cross-knowledge distillation method, a machine-learning technique that transfers knowledge from a larger teacher model to a smaller student model. This approach enhances a student model based on IMU data through cross-modal knowledge distillation from a skeleton-based model. By leveraging the skeleton-based model to extract insights on capturing spatial and temporal information on the body in time-series data and superior prediction capabilities, the student model is significantly improved in its ability to capture intricate and rapidly evolving motion patterns. This method enables the student model to assimilate salient features from the teacher model, thereby enhancing overall performance in HAR tasks.
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11851388
• 書誌情報: 研究報告モバイルコンピューティングと新社会システム（MBL）; 巻 2024-MBL-112, 号 3, p. 1-8, 発行日 2024-09-19
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8817
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会