{"id":"https://openalex.org/W2056003595","doi":"https://doi.org/10.1109/icmew.2013.6618294","title":"Simultaneously detect and segment pedestrian","display_name":"Simultaneously detect and segment pedestrian","publication_year":2013,"publication_date":"2013-07-01","ids":{"openalex":"https://openalex.org/W2056003595","doi":"https://doi.org/10.1109/icmew.2013.6618294","mag":"2056003595"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2013.6618294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2013.6618294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5115694804","display_name":"Shu Wang","orcid":"https://orcid.org/0009-0007-9385-3449"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shu Wang","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China","[Institute of Information Science, Beijing JiaoTong University, Beijing, China]"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"[Institute of Information Science, Beijing JiaoTong University, Beijing, China]","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100298934","display_name":"Zhenjiang Miao","orcid":"https://orcid.org/0000-0001-8032-5769"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenjiang Miao","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China","[Institute of Information Science, Beijing JiaoTong University, Beijing, China]"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]},{"raw_affiliation_string":"[Institute of Information Science, Beijing JiaoTong University, Beijing, China]","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100409994","display_name":"Jian Zhang","orcid":"https://orcid.org/0000-0002-7240-3541"},"institutions":[{"id":"https://openalex.org/I114017466","display_name":"University of Technology Sydney","ror":"https://ror.org/03f0f6041","country_code":"AU","type":"education","lineage":["https://openalex.org/I114017466"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jian Zhang","raw_affiliation_strings":["Advanced Analytics Institute, University of Technology, Sydney, Australia","Advanced Analytics Institute, University of Technology, Sydney, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"Advanced Analytics Institute, University of Technology, Sydney, Australia","institution_ids":["https://openalex.org/I114017466"]},{"raw_affiliation_string":"Advanced Analytics Institute, University of Technology, Sydney, NSW, Australia","institution_ids":["https://openalex.org/I114017466"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5115694804"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.12243311,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.644439697265625},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5684038996696472},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5168828964233398},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4574226140975952},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4421820044517517},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.15096276998519897},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14372193813323975}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.644439697265625},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5684038996696472},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5168828964233398},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4574226140975952},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4421820044517517},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.15096276998519897},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14372193813323975}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icmew.2013.6618294","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2013.6618294","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)","raw_type":"proceedings-article"},{"id":"pmh:oai:opus.lib.uts.edu.au:10453/28028","is_oa":false,"landing_page_url":"http://hdl.handle.net/10453/28028","pdf_url":null,"source":{"id":"https://openalex.org/S4306401357","display_name":"UTS ePRESS (University of Technology Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I114017466","host_organization_name":"University of Technology Sydney","host_organization_lineage":["https://openalex.org/I114017466"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Conference Proceeding"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7699999809265137,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W1998827347","https://openalex.org/W1999478155","https://openalex.org/W2030536784","https://openalex.org/W2108890589","https://openalex.org/W2120419212","https://openalex.org/W2132630629","https://openalex.org/W2161969291","https://openalex.org/W6676020639","https://openalex.org/W6680114966"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2058170566","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747"],"abstract_inverted_index":{"We":[0,19,89],"present":[1],"a":[2],"framework":[3],"to":[4,49,66],"simultaneously":[5],"detect":[6],"and":[7,81,97],"segment":[8,21],"pedestrian":[9],"in":[10],"images.":[11],"Our":[12],"work":[13],"is":[14,47],"based":[15],"on":[16,93],"part-based":[17],"method.":[18],"first":[20],"the":[22,35,51,55,68,75,83,94,98,101],"image":[23],"into":[24,29,61],"superpixels,":[25],"then":[26],"assemble":[27],"superpixels":[28],"body":[30,56,79,87],"part":[31,57],"candidates":[32,58],"by":[33],"comparing":[34],"assembled":[36],"shape":[37,44,52],"with":[38],"pre-built":[39],"template":[40],"library.":[41],"A":[42],"\u201cstructure-based\u201d":[43],"matching":[45],"algorithm":[46],"developed":[48],"measure":[50],"similarity.":[53],"All":[54],"are":[59],"input":[60],"our":[62,104],"modified":[63],"AND/OR":[64],"graph":[65,73],"generate":[67],"most":[69],"reasonable":[70],"combination.":[71],"The":[72],"describes":[74],"possible":[76],"variation":[77],"of":[78,103],"configuration":[80],"model":[82],"constrain":[84],"relationship":[85],"between":[86],"parts.":[88],"perform":[90],"comparison":[91],"experiments":[92],"public":[95],"database":[96],"results":[99],"show":[100],"effectiveness":[102],"framework.":[105]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
