{"id":"https://openalex.org/W2102603233","doi":"https://doi.org/10.1109/ivs.2014.6856439","title":"A SIFT-based mean shift algorithm for moving vehicle tracking","display_name":"A SIFT-based mean shift algorithm for moving vehicle tracking","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2102603233","doi":"https://doi.org/10.1109/ivs.2014.6856439","mag":"2102603233"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2014.6856439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2014.6856439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","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/A5078519973","display_name":"Wei Liang","orcid":"https://orcid.org/0000-0002-5074-1363"},"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":true,"raw_author_name":"Liang Wei","raw_affiliation_strings":["Tsinghua University, Beijing, Beijing, CN","Department of Automation, Tsinghua University, Beijing (China)"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing (China)","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103457968","display_name":"Xudong Xie","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":"Xie Xudong","raw_affiliation_strings":["Tsinghua University, Beijing, Beijing, CN","Department of Automation, Tsinghua University, Beijing (China)"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing (China)","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100420056","display_name":"Jianhua Wang","orcid":"https://orcid.org/0000-0003-4133-2776"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang Jianhua","raw_affiliation_strings":["Computer Teaching and Research group, Bethune medical college, 050000, China","Comput. Teaching & Res. group, Bethune Med. Coll., Shijiazhuang, China"],"affiliations":[{"raw_affiliation_string":"Computer Teaching and Research group, Bethune medical college, 050000, China","institution_ids":[]},{"raw_affiliation_string":"Comput. Teaching & Res. group, Bethune Med. Coll., Shijiazhuang, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336520","display_name":"Yi Zhang","orcid":"https://orcid.org/0009-0004-3895-0973"},"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":"Zhang Yi","raw_affiliation_strings":["Tsinghua University, Beijing, Beijing, CN","Department of Automation, Tsinghua University, Beijing (China)"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing (China)","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025327730","display_name":"Jianming Hu","orcid":"https://orcid.org/0000-0002-4418-605X"},"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":"Hu Jianming","raw_affiliation_strings":["Tsinghua University, Beijing, Beijing, CN","Department of Automation, Tsinghua University, Beijing (China)"],"affiliations":[{"raw_affiliation_string":"Tsinghua University, Beijing, Beijing, CN","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Department of Automation, Tsinghua University, Beijing (China)","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5078519973"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.9755,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.80981552,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"762","last_page":"767"},"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9883000254631042,"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/bhattacharyya-distance","display_name":"Bhattacharyya distance","score":0.8752084970474243},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.8661984205245972},{"id":"https://openalex.org/keywords/mean-shift","display_name":"Mean-shift","score":0.8093236684799194},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6749474406242371},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6140100359916687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6132338047027588},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6045259237289429},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.5574512481689453},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5537400245666504},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5261832475662231},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4482288360595703},{"id":"https://openalex.org/keywords/vehicle-tracking-system","display_name":"Vehicle tracking system","score":0.43795567750930786},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.432143896818161},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.4316604733467102},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.31983548402786255},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.29081130027770996},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.25262629985809326},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.10069367289543152}],"concepts":[{"id":"https://openalex.org/C24145651","wikidata":"https://www.wikidata.org/wiki/Q2901249","display_name":"Bhattacharyya distance","level":2,"score":0.8752084970474243},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.8661984205245972},{"id":"https://openalex.org/C48548287","wikidata":"https://www.wikidata.org/wiki/Q6803557","display_name":"Mean-shift","level":3,"score":0.8093236684799194},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6749474406242371},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6140100359916687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6132338047027588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6045259237289429},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.5574512481689453},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5537400245666504},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5261832475662231},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4482288360595703},{"id":"https://openalex.org/C84119951","wikidata":"https://www.wikidata.org/wiki/Q3498530","display_name":"Vehicle tracking system","level":3,"score":0.43795567750930786},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.432143896818161},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.4316604733467102},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.31983548402786255},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29081130027770996},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25262629985809326},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.10069367289543152},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2014.6856439","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2014.6856439","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE Intelligent Vehicles Symposium Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5699999928474426,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W162403846","https://openalex.org/W1982508891","https://openalex.org/W2062130980","https://openalex.org/W2063109511","https://openalex.org/W2067191022","https://openalex.org/W2118926449","https://openalex.org/W2126322717","https://openalex.org/W2132103241","https://openalex.org/W2139323304","https://openalex.org/W2151103935","https://openalex.org/W2164500538","https://openalex.org/W6678155448"],"related_works":["https://openalex.org/W2353430895","https://openalex.org/W1999186323","https://openalex.org/W2157169396","https://openalex.org/W2108433104","https://openalex.org/W2078413365","https://openalex.org/W2354022662","https://openalex.org/W2135926765","https://openalex.org/W2359566400","https://openalex.org/W2350505018","https://openalex.org/W2064428718"],"abstract_inverted_index":{"The":[0,162],"classical":[1],"mean":[2,29,61],"shift":[3,30,62],"algorithm":[4,31,63,154],"is":[5,13,32,64,80,93,107,113,155],"easy":[6],"to":[7,66,83,129],"pass":[8],"into":[9],"local":[10],"maxima,":[11],"which":[12,34],"caused":[14],"by":[15,99,116],"the":[16,47,50,53,60,68,77,84,90,96,110,121,130,134,138,147,153,167,176],"lack":[17],"of":[18,52,133,140,149,178],"appropriate":[19],"target":[20,70,97,111,122,143],"model":[21,98,112,123],"updating":[22],"mechanism.":[23],"In":[24,57,137],"this":[25],"paper,":[26],"a":[27,74,103],"SIFT-based":[28],"proposed,":[33],"can":[35,124,170],"be":[36,125],"used":[37],"for":[38],"continuous":[39],"vehicle":[40,54,142],"tracking":[41,78,88],"in":[42],"complex":[43],"situations,":[44],"such":[45,145],"as":[46,146],"shape":[48,182],"and":[49,72,102,120,151,157,181],"illumination":[51,180],"object":[55,174],"change.":[56],"our":[58],"algorithm,":[59],"utilized":[65],"determine":[67],"candidate":[69,91],"region,":[71],"then":[73],"judgment":[75],"on":[76],"effect":[79],"made":[81],"according":[82,128],"Bhattacharyya":[85],"coefficient.":[86],"If":[87],"fails,":[89],"area":[92],"matched":[94],"with":[95,159],"SIFT":[100,117],"feature,":[101],"new":[104],"track":[105,172],"position":[106],"determined.":[108],"Otherwise,":[109],"periodically":[114],"updated":[115,127],"feature":[118],"matching,":[119],"constantly":[126],"state":[131],"change":[132],"moving":[135,141],"vehicle.":[136],"scenes":[139],"deformations,":[144],"variation":[148],"scale":[150],"illumination,":[152],"tested":[156],"compared":[158],"other":[160],"algorithms.":[161],"experimental":[163],"results":[164],"show":[165],"that":[166],"proposed":[168],"method":[169],"effectively":[171],"an":[173],"under":[175],"condition":[177],"varying":[179],"deformation.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
