{"_buckets": {"deposit": "7f929b0a-8173-4fc5-ad99-008064ca21e9"}, "_deposit": {"id": "21712", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "21712"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00021712", "sets": ["314"]}, "author_link": ["64447", "64448", "64449", "64450", "64451", "64452", "64453", "64454", "64455", "64456", "64457", "64458", "64459", "64460"], "item_10_alternative_title_19": {"attribute_name": "その他のタイトル", "attribute_value_mlt": [{"subitem_alternative_title": "A study on a pedestrian detector adaptive to driving environment using in-vehicle camera and GPS", "subitem_alternative_title_language": "en"}]}, "item_10_biblio_info_6": {"attribute_name": "書誌情報", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2013-03", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "495", "bibliographicPageEnd": "66", "bibliographicPageStart": "61", "bibliographicVolumeNumber": "112", "bibliographic_titles": [{"bibliographic_title": "電子情報通信学会技術研究報告. PRMU, パターン認識・メディア理解", "bibliographic_titleLang": "ja"}]}]}, "item_10_description_4": {"attribute_name": "抄録", "attribute_value_mlt": [{"subitem_description": "近年,車載カメラ画像から歩行者を検出する研究が注目されている.しかし,時刻や天候,季節,走行場所などの環境要因によって歩行者の見えは大きく変動する.そのため,様々な環境で歩行者を高精度に検出することは難しい.画像中の歩行者の見えを特徴とした従来の検出手法では,事前に大量の学習用歩行者画像および非歩行者画像を人手で収集することで,このような変動に対応していた.しかし,あらゆる見えの変動を想定して学習用画像を用意することは難しい.そこで本報告では,車載カメラ映像から学習用画像を自動収集して歩行者検出器を走行環境に適応させる枠組みを提案し,この問題の解決を図る.具体的には,走行シーンを見えに基づいて分類し,GPSから得られる走行場所と関連付けて学習用の非歩行者画像を自動収集する.これにより,走行環境に適応させた歩行者検出器を構築する.同一経路を複数回走行して撮影した映像を用いて,環境適応させた歩行者検出器の精度を検証した.その結果,従来手法と比較して大幅な精度向上が見られ,提案手法の有効性を確認した.", "subitem_description_language": "ja", "subitem_description_type": "Abstract"}, {"subitem_description": "Recently, pedestrian detection from in-vehicle camera images is being focused. However, it is difficult to detect pedestrians accurately due to the variety of their appearances caused by various environmental factors. To deal with changes of driving environment, most existing appearance-based methods require the manual preparation of many pedestrian and non-pedestrian images. However, it is difficult to prepare training images considering all possible environmental factors. To tackle this problem, we propose a method to construct a pedestrian detector that is adaptive to driving environment by collecting non-pedestrian images automatically from in-vehicle camera video. The proposed method classifies driving scenes and then collects pedestrian images for each scene accompanied with GPS positions. We confirmed the effectiveness of the method compared to existing methods.", "subitem_description_language": "en", "subitem_description_type": "Abstract"}]}, "item_10_description_5": {"attribute_name": "内容記述", "attribute_value_mlt": [{"subitem_description": "IEICE Technical Report;PRMU2012-190", "subitem_description_language": "en", "subitem_description_type": "Other"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "URI", "subitem_identifier_uri": "http://ci.nii.ac.jp/naid/110009713415/"}, {"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/23861"}]}, "item_10_publisher_32": {"attribute_name": "出版者", "attribute_value_mlt": [{"subitem_publisher": "一般社団法人電子情報通信学会", "subitem_publisher_language": "ja"}]}, "item_10_relation_40": {"attribute_name": "シリーズ", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "IEICE Technical Report;PRMU2012-190"}]}]}, 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