{"created":"2021-03-01T06:29:23.602644+00:00","id":21699,"links":{},"metadata":{"_buckets":{"deposit":"c3e9864b-f483-42b6-a8d6-2fa0e73f8afc"},"_deposit":{"id":"21699","owners":[],"pid":{"revision_id":0,"type":"depid","value":"21699"},"status":"published"},"_oai":{"id":"oai:nagoya.repo.nii.ac.jp:00021699","sets":["312:313:314"]},"author_link":["64297","64298","64299","64300","64301","64302","64303","64304","64305","64306","64307","64308","64309","64310"],"item_10_alternative_title_19":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"A preliminary study on a pedestrian detector adaptive to the driving environment using an in-vehicle camera","subitem_alternative_title_language":"en"}]},"item_10_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2012-08","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"198","bibliographicPageEnd":"104","bibliographicPageStart":"99","bibliographicVolumeNumber":"112","bibliographic_titles":[{"bibliographic_title":"電子情報通信学会技術研究報告. IBISML, 情報論的学習理論と機械学習","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ため,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.本研究では,車載カメラ映像から学習用画像を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.本報告では検出精度に影響を与える主要な環境要因である走行場所の変化に着目し,走行場所毎に歩行者画像および非歩行者画像を収集することで走行環境適応型歩行者検出器を構築する手法を提案する.理想的に適応が行われた歩行者検出器の精度を検証したところ,従来手法と比較して大幅な精度向上が見られ,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 such as time, weather, season, and location. To deal with changes of pedestrians, pose and environment, most existing methods that makes use of their appearances require the preparation of a lot of pedestrian images manually. However, it is difficult to provide training images considering all possible environmental factors. To tackle this problem, we propose a method to make a pedestrian detector adaptive to a driving environment by collecting training images from in-vehicle camera images. We focused on the variety of the appearance per location that primarily affects the accuracy of pedestrian detection, and built an environment adaptive pedestrian detector by collecting pedestrians images and non-pedestrian images in each driving location. In evaluation experiments assuming ideal adaptation to the environment, we confirmed the effectiveness of the method conpared 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;PRMU2012-41,IEICE Technical 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