{"_buckets": {"deposit": "f9fc2dac-5033-4558-8567-878098a58dcb"}, "_deposit": {"id": "5159", "owners": [], "pid": {"revision_id": 0, "type": "depid", "value": "5159"}, "status": "published"}, "_oai": {"id": "oai:nagoya.repo.nii.ac.jp:00005159"}, "item_10_biblio_info_6": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2004-09", "bibliographicIssueDateType": "Issued"}, "bibliographicIssueNumber": "3", "bibliographicPageEnd": "154", "bibliographicPageStart": "142", "bibliographicVolumeNumber": "5", "bibliographic_titles": [{"bibliographic_title": "IEEE Transactions on Intelligent Transportation Systems"}]}]}, "item_10_description_4": {"attribute_name": "\u6284\u9332", "attribute_value_mlt": [{"subitem_description": "Shadows of moving objects often obstruct robust visual tracking. In this paper, we present a car tracker based on a hidden Markov model/Markov random field (HMM/MRF)-based segmentation method that is capable of classifying each small region\nof an image into three different categories: vehicles, shadows of vehicles, and background from a traffic-monitoring movie. The temporal continuity of the different categories for one small region location is modeled as a single HMM along the time axis, independently of the neighboring regions. In order to incorporate\nspatial-dependent information among neighboring regions into the tracking process, at the state-estimation stage, the output from the HMMs is regarded as an MRF and the maximum a posteriori criterion is employed in conjunction with the MRF for optimization. At each time step, the state estimation for the image is equivalent to the optimal configuration of the MRF generated through a stochastic relaxation process. Experimental results show that, using this method, foreground (vehicles) and nonforeground regions including the shadows of moving vehicles can be discriminated with high accuracy.", "subitem_description_type": "Abstract"}]}, "item_10_identifier_60": {"attribute_name": "URI", "attribute_value_mlt": [{"subitem_identifier_type": "HDL", "subitem_identifier_uri": "http://hdl.handle.net/2237/6743"}]}, "item_10_publisher_32": {"attribute_name": "\u51fa\u7248\u8005", "attribute_value_mlt": [{"subitem_publisher": "IEEE"}]}, "item_10_relation_11": {"attribute_name": "DOI", "attribute_value_mlt": [{"subitem_relation_type_id": {"subitem_relation_type_id_text": "http://dx.doi.org/10.1109/TITS.2004.833791", "subitem_relation_type_select": "DOI"}}]}, "item_10_rights_12": {"attribute_name": "\u6a29\u5229", "attribute_value_mlt": [{"subitem_rights": "Copyright \u00a9 2004 IEEE. Reprinted from (relevant publication info). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Nagoya University\u2019s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org."}]}, "item_10_select_15": {"attribute_name": "\u8457\u8005\u7248\u30d5\u30e9\u30b0", "attribute_value_mlt": [{"subitem_select_item": "publisher"}]}, "item_10_source_id_7": {"attribute_name": "ISSN", "attribute_value_mlt": [{"subitem_source_identifier": "1524-9050", "subitem_source_identifier_type": "ISSN"}]}, "item_10_text_14": {"attribute_name": "\u30d5\u30a9\u30fc\u30de\u30c3\u30c8", "attribute_value_mlt": [{"subitem_text_value": "application/pdf"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "Kato, Jien"}], "nameIdentifiers": [{"nameIdentifier": "12905", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Watanabe, Toyohide"}], "nameIdentifiers": [{"nameIdentifier": "12906", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Joga, S\u00e9bastien"}], "nameIdentifiers": [{"nameIdentifier": "12907", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Ying, Liu"}], "nameIdentifiers": [{"nameIdentifier": "12908", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "Hase, Hiroyuki"}], "nameIdentifiers": [{"nameIdentifier": "12909", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u52a0\u85e4, \u30b8\u30a7\u30fc\u30f3"}], "nameIdentifiers": [{"nameIdentifier": "12910", "nameIdentifierScheme": "WEKO"}]}, {"creatorNames": [{"creatorName": "\u6e21\u9089, \u8c4a\u82f1"}], "nameIdentifiers": [{"nameIdentifier": "12911", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2018-02-19"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "01331385.pdf", "filesize": [{"value": "1.0 MB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 1000000.0, "url": {"label": "01331385.pdf", "url": "https://nagoya.repo.nii.ac.jp/record/5159/files/01331385.pdf"}, "version_id": "9c0debda-e5ae-4035-a530-dd9711f53978"}]}, "item_keyword": {"attribute_name": "\u30ad\u30fc\u30ef\u30fc\u30c9", "attribute_value_mlt": [{"subitem_subject": "Hidden Markov model (HMM)", "subitem_subject_scheme": "Other"}, {"subitem_subject": "image classification", "subitem_subject_scheme": "Other"}, {"subitem_subject": "image segmentation", "subitem_subject_scheme": "Other"}, {"subitem_subject": "Markov random field (MRF)", "subitem_subject_scheme": "Other"}, {"subitem_subject": "traffic surveillance", "subitem_subject_scheme": "Other"}, {"subitem_subject": "vehicle tracking", "subitem_subject_scheme": "Other"}]}, "item_language": {"attribute_name": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "eng"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "journal article", "resourceuri": "http://purl.org/coar/resource_type/c_6501"}]}, "item_title": "An HMM/MRF-based stochastic framework for robust vehicle tracking", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "An HMM/MRF-based stochastic framework for robust vehicle tracking"}]}, "item_type_id": "10", "owner": "1", "path": ["312/313/314"], "permalink_uri": "http://hdl.handle.net/2237/6743", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2006-07-26"}, "publish_date": "2006-07-26", "publish_status": "0", "recid": "5159", "relation": {}, "relation_version_is_last": true, "title": ["An HMM/MRF-based stochastic framework for robust vehicle tracking"], "weko_shared_id": 3}