=== Generating (published_papers) ===
=== Generating (teaching_experience) ===
=== Generating (misc) ===
=== Generating (works) ===
=== Generating (industrial_property_rights) ===
=== Generating (awards) ===
=== Generating (association_memberships) ===
=== Generating (presentations) ===
==== begin registerFile(/WWW/pub2/data/ERD/person/10767/researchmap/published_papers.jsonl) ====
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line:5, {"insert":{"user_id":"5000091366","type":"published_papers","id":"30612692"},"force":{"see_also":[{"@id":"https://www.scopus.com/record/display.url?eid=2-s2.0-85090460132&origin=inward","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=371919","label":"url"}],"paper_title":{"en":"Prediction of the VDT Worker's Headache Using Convolutional Neural Network with Class Activation Mapping","ja":"Prediction of the VDT Worker's Headache Using Convolutional Neural Network with Class Activation Mapping"},"authors":{"en":[{"name":"Elsayed Sharara"},{"name":"Tsuji Akinori"},{"name":"Karungaru Stephen"},{"name":"Terada Kenji"}],"ja":[{"name":"Elsayed Sharara"},{"name":"辻 明典"},{"name":"カルンガル スティフィン"},{"name":"寺田 賢治"}]},"description":{"en":"本論文ではVDT作業者の疲労具合を検知するためにカメラ映像から6つの手のポーズを分類する手法を提案する．提案手法では，手のポーズの分類に畳み込みニューラルネットワーク(CNN)をベースとしたディープラーニングを適用している．さらに，顔面上の手のポーズの定位のための分類ネットワークの予測を可視化するためのクラス活性化マップ(CAM)を実装した．有効性を検証する実験で，98.5%の精度で認識できることがわかった．","ja":"本論文ではVDT作業者の疲労具合を検知するためにカメラ映像から6つの手のポーズを分類する手法を提案する．提案手法では，手のポーズの分類に畳み込みニューラルネットワーク(CNN)をベースとしたディープラーニングを適用している．さらに，顔面上の手のポーズの定位のための分類ネットワークの予測を可視化するためのクラス活性化マップ(CAM)を実装した．有効性を検証する実験で，98.5%の精度で認識できることがわかった．"},"publication_date":"2020-10-25","publication_name":{"en":"IEEJ Transactions on Electrical and Electronic Engineering","ja":"IEEJ Transactions on Electrical and Electronic Engineering"},"volume":"Vol.15","number":"No.11","starting_page":"1690","ending_page":"1697","languages":["eng"],"referee":true,"identifiers":{"doi":["10.1002/tee.23239"],"issn":["1931-4973"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
line:6, {"insert":{"user_id":"5000091366","type":"published_papers","id":"30400674"},"force":{"see_also":[{"@id":"https://repo.lib.tokushima-u.ac.jp/ja/115188","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=337288","label":"url"}],"paper_title":{"en":"Human Detection and Tracking Using HOG Feature and Particle Filter in Video Surveillance System","ja":"Human Detection and Tracking Using HOG Feature and Particle Filter in Video Surveillance System"},"authors":{"en":[{"name":"Bhuyain Mobarok Hossain"},{"name":"Karungaru Stephen"},{"name":"Terada Kenji"},{"name":"Tsuji Akinori"}],"ja":[{"name":"BHUYAIN MOBAROK HOSSAIN"},{"name":"カルンガル スティフィン"},{"name":"寺田 賢治"},{"name":"辻 明典"}]},"description":{"en":"監視カメラから得られる映像を用いて，HOG特徴とパーティクルフィルタを用いて人間を抽出し，さらに追跡する手法を提案する．本手法ではHOG特徴を用いて人の領域を抽出し，さらにパーティクルフィルタを用いて人の動きを追跡する．これにより複雑な背景においても，複数の人を同時に検出と追跡することが可能である．本手法の有効性を検証するために，4つのシーンに適用したところ，約90%の検出精度と約99%の追跡精度が得られることがわかった．","ja":"監視カメラから得られる映像を用いて，HOG特徴とパーティクルフィルタを用いて人間を抽出し，さらに追跡する手法を提案する．本手法ではHOG特徴を用いて人の領域を抽出し，さらにパーティクルフィルタを用いて人の動きを追跡する．これにより複雑な背景においても，複数の人を同時に検出と追跡することが可能である．本手法の有効性を検証するために，4つのシーンに適用したところ，約90%の検出精度と約99%の追跡精度が得られることがわかった．"},"publication_date":"2018-04-01","publication_name":{"en":"International Journal of Advanced Intelligence (IJAI)","ja":"International Journal of Advanced Intelligence (IJAI)"},"volume":"Vol.9","number":"No.3","starting_page":"397","ending_page":"407","languages":["eng"],"referee":true,"identifiers":{"issn":["1883-3918"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
line:7, {"insert":{"user_id":"5000091366","type":"published_papers"},"similar_merge":{"see_also":[{"@id":"https://repo.lib.tokushima-u.ac.jp/ja/112227","label":"url"},{"@id":"https://cir.nii.ac.jp/crid/1050564287703187072/","label":"url"},{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=338025","label":"url"}],"paper_title":{"en":"A Practice for Information Technology Education Using Teaching Materials Based on the Physical Computing","ja":"フィジカルコンピューティング教材を用いた情報技術教育の実践"},"authors":{"en":[{"name":"Tsuji Akinori"},{"name":"Koori Norihiko"},{"name":"Kawakami Hiroshi"}],"ja":[{"name":"辻 明典"},{"name":"桑折 範彦"},{"name":"川上 博"}]},"publication_date":"2018-03","publication_name":{"en":"Journal of University Extension","ja":"徳島大学大学開放実践センター紀要"},"volume":"Vol.27","starting_page":"23","ending_page":"30","languages":["jpn"],"referee":true,"identifiers":{"issn":["0915-8685"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
line:8, {"insert":{"user_id":"5000091366","type":"published_papers"},"similar_merge":{"see_also":[{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=334914","label":"url"}],"paper_title":{"en":"Motion-Based Detection and Tracking of Multiple Pedestrians","ja":"Motion-Based Detection and Tracking of Multiple Pedestrians"},"authors":{"en":[{"name":"Abdel Rahman Maher Harras"},{"name":"Tsuji Akinori"},{"name":"Terada Kenji"}],"ja":[{"name":"Abdel Rahman Maher Harras"},{"name":"辻 明典"},{"name":"寺田 賢治"}]},"description":{"en":"本論文では，GMMとカルマンフィルタを用いて，通行人数を進行方向別に認識する手法を提案した．本手法では，GMMを用いることで連続する映像の中から動きのある人を検出し，さらにカルマンフィルタを用いて人を追跡することで，進行方向別の通行者の人数を計測することができる．本手法の有効性を検証するために，実際のシーンに対する実験を行なったところ，高い精度で認識するできることが確認できた．","ja":"本論文では，GMMとカルマンフィルタを用いて，通行人数を進行方向別に認識する手法を提案した．本手法では，GMMを用いることで連続する映像の中から動きのある人を検出し，さらにカルマンフィルタを用いて人を追跡することで，進行方向別の通行者の人数を計測することができる．本手法の有効性を検証するために，実際のシーンに対する実験を行なったところ，高い精度で認識するできることが確認できた．"},"publication_date":"2017-12-01","publication_name":{"en":"International Journal of Mechanical and Materials Engineering","ja":"International Journal of Mechanical and Materials Engineering"},"volume":"Vol.11","number":"No.12","starting_page":"1792","ending_page":"1795","languages":["eng"],"referee":true,"identifiers":{"doi":["10.5281/zenodo.1314459"]},"published_paper_type":"scientific_journal"},"priority":"input_data"}
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line:4, {"insert":{"user_id":"5000091366","type":"presentations","id":"30400691"},"force":{"see_also":[{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=363253","label":"url"}],"presentation_title":{"en":"Detection of Various Vehicles Using YOLO Object Detector","ja":"Detection of Various Vehicles Using YOLO Object Detector"},"presenters":{"en":[{"name":"Abdelrahman Maher Abdelkader Harras"},{"name":"Tsuji Akinori"},{"name":"Terada Kenji"}],"ja":[{"name":"Abdelrahman Maher Abdelkader Harras"},{"name":"辻 明典"},{"name":"寺田 賢治"}]},"event":{"en":"Proceedings of the 26th International Workshop on Frontiers of Computer Vision (IW-FCV 2020)","ja":"Proceedings of the 26th International Workshop on Frontiers of Computer Vision (IW-FCV 2020)"},"publication_date":"2020-02-20","languages":["eng"],"location":{"en":"Kagoshima","ja":"Kagoshima"},"description":{"en":"本論文では，YOLO(You Look Only Onse)アルゴリズムを用いて多種多様な自動車を検出する手法を提案する．この手法では，従来あるような検出窓を走査させることなく，自動車が映っている画像にCNN(Convolutional Neural Network)を適用することで，色や形，大きさの異なる多種多様な自動車を検出することが可能である．さらに実際の多種多様な自動車が映っている前方車載カメラ映像に対して本手法を適用して有効性を検証した実験について示した．","ja":"本論文では，YOLO(You Look Only Onse)アルゴリズムを用いて多種多様な自動車を検出する手法を提案する．この手法では，従来あるような検出窓を走査させることなく，自動車が映っている画像にCNN(Convolutional Neural Network)を適用することで，色や形，大きさの異なる多種多様な自動車を検出することが可能である．さらに実際の多種多様な自動車が映っている前方車載カメラ映像に対して本手法を適用して有効性を検証した実験について示した．"},"is_international_presentation":true},"priority":"input_data"}
line:5, {"insert":{"user_id":"5000091366","type":"presentations","id":"30400692"},"force":{"see_also":[{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=352871","label":"url"}],"presentation_title":{"en":"Drowsiness Detection by Facial Features and Hand Gestures Using Deep learning","ja":"Drowsiness Detection by Facial Features and Hand Gestures Using Deep learning"},"presenters":{"en":[{"name":"Elsayed Abdelghany Mohamed Abousharara"},{"name":"Tsuji Akinori"},{"name":"Terada Kenji"}],"ja":[{"name":"ELSAYED ABDELGHANY MOHAMED ABOUSHARARA"},{"name":"辻 明典"},{"name":"寺田 賢治"}]},"event":{"en":"The International Council on Electrical Engineering Conference 2019(ICEE2019)","ja":"The International Council on Electrical Engineering Conference 2019(ICEE2019)"},"publication_date":"2019-07-05","languages":["eng"],"location":{"en":"Hong Kong","ja":"Hong Kong"},"description":{"en":"Drowsiness detection is one of the interesting research for computer vision regarding drivers fatigue. Previous studies of drowsiness detection has mainly focused on tracking of the eyes and mouth conditions like eyes blinking, yawing, and so on. The detection of hand gesture is also commonly used as the sign of drowsiness such as the hand cover on mouth and with the closure of the eyes by this hand gesture sign. The applications of this research can expand from only the observation of drowsiness for drivers to observing the status of drowsiness at other environments, for example, call center agents, control room observers, and air traffic controllers. Our proposed system integrates traditional machine learning and deep learning algorithms to increase the performance of accurate detection using four stages as the following. The first stage is the detection of eyes and mouth in the scene. The second stage is skin color detection to extract the face and hands area. The third stage is classification using deep learning to recognize the status of person. Final fourth stage is to detect the signs of drowsiness automatically. For the eyes and mouth detection, our system used a feature detection based on the Haar cascade classifiers. YCbCr color space is used for the skin-color detection if the mouth detection is failed. Three transfer learning convolutional neural network (CNN) models in order to classify the status of eyes, mouth and hand gestures. The images for evaluation were processed by MATLAB, we applied GoogLeNet learned by three datasets consists of eyes opened and closed, mouth opened and closed, and hand over mouth images. Finally, the drowsiness is successfully recognized in the following cases: (1) The eyes are closed more than normal blinking rate and less than the full closure due to sleep and open the mouth. (2) The eyes are closed and the hand covered on the mouth. The system achieved the classification ratio of drowsiness signs average 96.6 %.","ja":"Drowsiness detection is one of the interesting research for computer vision regarding drivers fatigue. Previous studies of drowsiness detection has mainly focused on tracking of the eyes and mouth conditions like eyes blinking, yawing, and so on. The detection of hand gesture is also commonly used as the sign of drowsiness such as the hand cover on mouth and with the closure of the eyes by this hand gesture sign. The applications of this research can expand from only the observation of drowsiness for drivers to observing the status of drowsiness at other environments, for example, call center agents, control room observers, and air traffic controllers. Our proposed system integrates traditional machine learning and deep learning algorithms to increase the performance of accurate detection using four stages as the following. The first stage is the detection of eyes and mouth in the scene. The second stage is skin color detection to extract the face and hands area. The third stage is classification using deep learning to recognize the status of person. Final fourth stage is to detect the signs of drowsiness automatically. For the eyes and mouth detection, our system used a feature detection based on the Haar cascade classifiers. YCbCr color space is used for the skin-color detection if the mouth detection is failed. Three transfer learning convolutional neural network (CNN) models in order to classify the status of eyes, mouth and hand gestures. The images for evaluation were processed by MATLAB, we applied GoogLeNet learned by three datasets consists of eyes opened and closed, mouth opened and closed, and hand over mouth images. Finally, the drowsiness is successfully recognized in the following cases: (1) The eyes are closed more than normal blinking rate and less than the full closure due to sleep and open the mouth. (2) The eyes are closed and the hand covered on the mouth. The system achieved the classification ratio of drowsiness signs average 96.6 %."},"is_international_presentation":true},"priority":"input_data"}
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line:7, {"insert":{"user_id":"5000091366","type":"presentations","id":"30400694"},"force":{"see_also":[{"@id":"https://web.db.tokushima-u.ac.jp/cgi-bin/edb_browse?EID=350397","label":"url"}],"presentation_title":{"en":"Advanced Prevention for \"KAROSHI\" using Artificial Intelligence","ja":"Advanced Prevention for \"KAROSHI\" using Artificial Intelligence"},"presenters":{"en":[{"name":"Elsayed Abdelghany Mohamed Abousharara"},{"name":"Tsuji Akinori"},{"name":"Terada Kenji"}],"ja":[{"name":"ELSAYED ABDELGHANY MOHAMED ABOUSHARARA"},{"name":"辻 明典"},{"name":"寺田 賢治"}]},"event":{"en":"Nature's 150th Anniversary Symposium","ja":"Nature's 150th Anniversary Symposium"},"publication_date":"2019-04-04","languages":["eng"],"location":{"en":"Tokyo","ja":"Tokyo"},"description":{"en":"KAROSHI, death from overwork, is a well-known word, especially in Japan. There are government efforts to prevent the occurrence of KAROSHI, however, Japanese still suffer as log hours work persist. In the current research, our system can be recognized several facial features and body signs regarding of stress and fatigure caused by overwork such as a headache, neck , shoulder, and back pain. Of the recognition,we used a general webcam with PC to implemet the acritical intelligence algorithm. Our AI system has achieved the recognition of stress and fatigure signs precisely with high accuracy of 95.0%. This system contributes to restrain Japanese KAROSHI in the near future.","ja":"KAROSHI, death from overwork, is a well-known word, especially in Japan. There are government efforts to prevent the occurrence of KAROSHI, however, Japanese still suffer as log hours work persist. In the current research, our system can be recognized several facial features and body signs regarding of stress and fatigure caused by overwork such as a headache, neck , shoulder, and back pain. Of the recognition,we used a general webcam with PC to implemet the acritical intelligence algorithm. Our AI system has achieved the recognition of stress and fatigure signs precisely with high accuracy of 95.0%. This system contributes to restrain Japanese KAROSHI in the near future."},"is_international_presentation":true},"priority":"input_data"}
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