{"id":"https://openalex.org/W2115574509","doi":"https://doi.org/10.1109/tits.2009.2018961","title":"Real-Time Pedestrian Detection and Tracking at Nighttime for Driver-Assistance Systems","display_name":"Real-Time Pedestrian Detection and Tracking at Nighttime for Driver-Assistance Systems","publication_year":2009,"publication_date":"2009-06-01","ids":{"openalex":"https://openalex.org/W2115574509","doi":"https://doi.org/10.1109/tits.2009.2018961","mag":"2115574509"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2009.2018961","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2009.2018961","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5100765368","display_name":"Junfeng Ge","orcid":"https://orcid.org/0000-0003-1572-8611"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junfeng Ge","raw_affiliation_strings":["Tsinghua National Laboratory of Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China","Dept. of Autom., Tsinghua Univ., Beijing#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua National Laboratory of Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Autom., Tsinghua Univ., Beijing#TAB#","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113653028","display_name":"Yupin Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yupin Luo","raw_affiliation_strings":["Tsinghua National Laboratory of Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China","Dept. of Autom., Tsinghua Univ., Beijing#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua National Laboratory of Information Science and Technology, Department of Automation, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Dept. of Autom., Tsinghua Univ., Beijing#TAB#","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014154875","display_name":"Gyomei Tei","orcid":null},"institutions":[{"id":"https://openalex.org/I4210139227","display_name":"CF Technologies (United States)","ror":"https://ror.org/03gc99a64","country_code":"US","type":"company","lineage":["https://openalex.org/I4210139227"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gyomei Tei","raw_affiliation_strings":["INF Technologies Limited, Beijing, China","INF Technologies Ltd., Beijing, China#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"INF Technologies Limited, Beijing, China","institution_ids":[]},{"raw_affiliation_string":"INF Technologies Ltd., Beijing, China#TAB#","institution_ids":["https://openalex.org/I4210139227"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.0525,"has_fulltext":false,"cited_by_count":171,"citation_normalized_percentile":{"value":0.98244814,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":"10","issue":"2","first_page":"283","last_page":"298"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9984999895095825,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9937999844551086,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/pedestrian-detection","display_name":"Pedestrian detection","score":0.8318170309066772},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7912988662719727},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7336533069610596},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.723082423210144},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5671719312667847},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.5517765879631042},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.5266373157501221},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.5239410996437073},{"id":"https://openalex.org/keywords/cascading-classifiers","display_name":"Cascading classifiers","score":0.5132715702056885},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4903676509857178},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.421223521232605},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.37498021125793457},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34283602237701416},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14441365003585815},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13316214084625244},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.10909396409988403}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8318170309066772},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7912988662719727},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7336533069610596},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.723082423210144},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5671719312667847},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.5517765879631042},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.5266373157501221},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.5239410996437073},{"id":"https://openalex.org/C40651066","wikidata":"https://www.wikidata.org/wiki/Q5048220","display_name":"Cascading classifiers","level":4,"score":0.5132715702056885},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4903676509857178},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.421223521232605},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.37498021125793457},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34283602237701416},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14441365003585815},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13316214084625244},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.10909396409988403},{"id":"https://openalex.org/C106135958","wikidata":"https://www.wikidata.org/wiki/Q7291993","display_name":"Random subspace method","level":3,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2009.2018961","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2009.2018961","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8299999833106995}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W396735907","https://openalex.org/W1541438145","https://openalex.org/W1559644488","https://openalex.org/W1563060484","https://openalex.org/W1608462934","https://openalex.org/W1849274298","https://openalex.org/W1863635071","https://openalex.org/W1975846642","https://openalex.org/W1992825118","https://openalex.org/W2024046085","https://openalex.org/W2032210760","https://openalex.org/W2036490799","https://openalex.org/W2099355420","https://openalex.org/W2106300334","https://openalex.org/W2112385363","https://openalex.org/W2115763357","https://openalex.org/W2117662993","https://openalex.org/W2125896931","https://openalex.org/W2129317335","https://openalex.org/W2133003941","https://openalex.org/W2133984628","https://openalex.org/W2137545186","https://openalex.org/W2142645932","https://openalex.org/W2143026545","https://openalex.org/W2145023731","https://openalex.org/W2151624311","https://openalex.org/W2154740476","https://openalex.org/W2159686933","https://openalex.org/W2160127501","https://openalex.org/W2161969291","https://openalex.org/W2163272476","https://openalex.org/W2164598857","https://openalex.org/W2166623283","https://openalex.org/W2168599433","https://openalex.org/W2169897982","https://openalex.org/W2170248718","https://openalex.org/W2799061466","https://openalex.org/W4244494905","https://openalex.org/W4244952642","https://openalex.org/W6633484906","https://openalex.org/W6676584015","https://openalex.org/W6677320435"],"related_works":["https://openalex.org/W1977648593","https://openalex.org/W2348780717","https://openalex.org/W2780165746","https://openalex.org/W4235475963","https://openalex.org/W4252030899","https://openalex.org/W3087821047","https://openalex.org/W2602033589","https://openalex.org/W2624903463","https://openalex.org/W3097905314","https://openalex.org/W1982577389"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,24,152,185],"is":[2,90,100,112,156,161,223],"one":[3],"of":[4,66,125,187,194,232],"the":[5,57,60,64,71,75,80,94,105,116,131,146,151,165,180,192,204,220,230],"most":[6],"important":[7],"components":[8],"in":[9,46,63,83,104,140],"driver-assistance":[10],"systems.":[11],"In":[12],"this":[13],"paper,":[14],"we":[15],"propose":[16],"a":[17,31,47,108,141,184,210],"monocular":[18],"vision":[19],"system":[20,222],"for":[21,225],"real-time":[22],"pedestrian":[23,106],"and":[25,42,49,129,136,149,158,175,199,228],"tracking":[26,155],"during":[27],"nighttime":[28,84],"driving":[29],"with":[30],"near-infrared":[32],"(NIR)":[33],"camera.":[34],"Three":[35],"modules":[36],"(region-of-interest":[37],"(ROI)":[38],"generation,":[39],"object":[40],"classification,":[41],"tracking)":[43],"are":[44],"integrated":[45],"cascade,":[48],"each":[50],"utilizes":[51],"complementary":[52],"visual":[53],"features":[54,139],"to":[55,114,163],"distinguish":[56],"objects":[58,76],"from":[59,169],"cluttered":[61],"background":[62,82],"range":[65],"20-80":[67],"m.":[68],"Based":[69],"on":[70,93,122,134,172,209],"common":[72],"fact":[73],"that":[74,179,219],"appear":[77],"brighter":[78],"than":[79,189],"nearby":[81],"NIR":[85],"images,":[86],"efficient":[87],"ROI":[88],"generation":[89],"done":[91],"based":[92,133],"dual-threshold":[95],"segmentation":[96],"algorithm.":[97],"As":[98],"there":[99],"large":[101],"intraclass":[102],"variability":[103],"class,":[107],"tree-structured,":[109],"two-stage":[110],"detector":[111],"proposed":[113,221],"tackle":[115],"problem":[117],"through":[118],"training":[119],"separate":[120],"classifiers":[121,132],"disjoint":[123],"subsets":[124],"different":[126],"image":[127],"sizes":[128],"arranging":[130],"Haar-like":[135],"histogram-of-oriented-gradients":[137],"(HOG)":[138],"coarse-to-fine":[142],"manner.":[143],"To":[144],"suppress":[145],"false":[147,197],"alarms":[148],"fill":[150],"gaps,":[153],"template-matching-based":[154],"adopted,":[157],"multiframe":[159],"validation":[160],"used":[162],"obtain":[164],"final":[166],"results.":[167],"Results":[168],"extensive":[170],"tests":[171],"both":[173],"urban":[174],"suburban":[176],"videos":[177],"indicate":[178],"algorithm":[181],"can":[182],"produce":[183],"rate":[186,206],"more":[188],"90%":[190],"at":[191],"cost":[193],"about":[195],"10":[196],"alarms/h":[198],"perform":[200],"as":[201,203],"fast":[202],"frame":[205],"(30":[207],"frames/s)":[208],"Pentium":[211],"IV":[212],"3.0-GHz":[213],"personal":[214],"computer,":[215],"which":[216],"also":[217],"demonstrates":[218],"feasible":[224],"practical":[226],"applications":[227],"enjoys":[229],"advantage":[231],"low":[233],"implementation":[234],"cost.":[235]},"counts_by_year":[{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":8},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":10},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":13},{"year":2017,"cited_by_count":11},{"year":2016,"cited_by_count":12},{"year":2015,"cited_by_count":15},{"year":2014,"cited_by_count":13},{"year":2013,"cited_by_count":16},{"year":2012,"cited_by_count":10}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}