{"id":"https://openalex.org/W2754760580","doi":"https://doi.org/10.1109/icip.2017.8296461","title":"Deep learning architecture for pedestrian 3-D localization and tracking using multiple cameras","display_name":"Deep learning architecture for pedestrian 3-D localization and tracking using multiple cameras","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2754760580","doi":"https://doi.org/10.1109/icip.2017.8296461","mag":"2754760580"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8296461","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296461","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5045577049","display_name":"Kikyung Kim","orcid":"https://orcid.org/0000-0001-7118-9929"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kikyung Kim","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Seoul National University, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079048252","display_name":"Byeongho Heo","orcid":"https://orcid.org/0000-0003-2134-6345"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Byeongho Heo","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Seoul National University, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044484868","display_name":"Moonsub Byeon","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Moonsub Byeon","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Seoul National University, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5088891890","display_name":"Jin Young Choi","orcid":"https://orcid.org/0000-0003-3891-5815"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jin Young Choi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Seoul National University, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Seoul National University, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1147","last_page":"1151"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9986000061035156,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7383695840835571},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.72670978307724},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7249341607093811},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6889866590499878},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6454881429672241},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6131788492202759},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5492773056030273},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.5145092010498047},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.4727799892425537},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4300433397293091},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14486479759216309},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.10494014620780945}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7383695840835571},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.72670978307724},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7249341607093811},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6889866590499878},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6454881429672241},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6131788492202759},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5492773056030273},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.5145092010498047},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.4727799892425537},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4300433397293091},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14486479759216309},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.10494014620780945},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8296461","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8296461","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7099999785423279}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W2031454541","https://openalex.org/W2055366607","https://openalex.org/W2084652104","https://openalex.org/W2113697988","https://openalex.org/W2120419212","https://openalex.org/W2122088301","https://openalex.org/W2124535811","https://openalex.org/W2135841119","https://openalex.org/W2141822239","https://openalex.org/W2158634074","https://openalex.org/W2161969291","https://openalex.org/W2170101770","https://openalex.org/W2170296202","https://openalex.org/W2171243491","https://openalex.org/W2327356697","https://openalex.org/W2475609615","https://openalex.org/W6631782140","https://openalex.org/W6684811926","https://openalex.org/W6721232570"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W3122828758","https://openalex.org/W2170799233","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"In":[0,56,98],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,16,50],"novel":[6],"deep-learning":[7,22],"architecture":[8],"for":[9],"accurate":[10],"3-D":[11,86,112],"localization":[12,32,43,87,113],"and":[13,31,41,74,88,114],"tracking":[14,89],"of":[15,26,49,111],"pedestrian":[17,39,51],"using":[18],"multiple":[19],"cameras.":[20],"The":[21,34],"network":[23,30,36,44,95],"is":[24,62,91,102],"composed":[25],"two":[27,68,82],"networks:":[28],"detection":[29,35,54,72],"network.":[33],"yields":[37],"the":[38,42,46,67,71,81,99,105,109],"detections":[40],"estimates":[45],"ground":[47],"position":[48],"within":[52],"its":[53],"box.":[55],"addition,":[57],"an":[58],"attentional":[59],"pass":[60],"filter":[61],"introduced":[63],"to":[64],"effectively":[65],"connect":[66],"networks.":[69],"Using":[70],"proposals":[73],"their":[75],"2-D":[76],"grounding":[77],"positions":[78],"obtained":[79],"from":[80],"networks,":[83],"multi-camera":[84],"multi-target":[85],"algorithm":[90],"developed":[92],"through":[93],"min-cost":[94],"flow":[96],"approach.":[97],"experiments,":[100],"it":[101],"shown":[103],"that":[104],"proposed":[106],"method":[107],"improves":[108],"performance":[110],"tracking.":[115]},"counts_by_year":[{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}