{"id":"https://openalex.org/W2549628240","doi":"https://doi.org/10.1109/avss.2016.7738065","title":"Visual tracking based on object appearance and structure preserved local patches matching","display_name":"Visual tracking based on object appearance and structure preserved local patches matching","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2549628240","doi":"https://doi.org/10.1109/avss.2016.7738065","mag":"2549628240"},"language":"en","primary_location":{"id":"doi:10.1109/avss.2016.7738065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss.2016.7738065","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","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/A5100776069","display_name":"Wei Wang","orcid":"https://orcid.org/0000-0002-3421-3835"},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wei Wang","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045314081","display_name":"Kun Duan","orcid":"https://orcid.org/0000-0003-3897-4081"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kun Duan","raw_affiliation_strings":["Snap Chat Inc"],"affiliations":[{"raw_affiliation_string":"Snap Chat Inc","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109225019","display_name":"Tai-Peng Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tai-Peng Tian","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101063340","display_name":"Ting Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ting Yu","raw_affiliation_strings":["Alphabet Inc"],"affiliations":[{"raw_affiliation_string":"Alphabet Inc","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113875926","display_name":"Ser Nam Lim","orcid":null},"institutions":[{"id":"https://openalex.org/I4210134512","display_name":"GE Global Research (United States)","ror":"https://ror.org/03e06qt98","country_code":"US","type":"company","lineage":["https://openalex.org/I4210134512"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ser Nam Lim","raw_affiliation_strings":["GE Global Research"],"affiliations":[{"raw_affiliation_string":"GE Global Research","institution_ids":["https://openalex.org/I4210134512"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072730926","display_name":"Hairong Qi","orcid":"https://orcid.org/0000-0002-2693-5520"},"institutions":[{"id":"https://openalex.org/I75027704","display_name":"University of Tennessee at Knoxville","ror":"https://ror.org/020f3ap87","country_code":"US","type":"education","lineage":["https://openalex.org/I75027704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hairong Qi","raw_affiliation_strings":["University of Tennessee"],"affiliations":[{"raw_affiliation_string":"University of Tennessee","institution_ids":["https://openalex.org/I75027704"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100776069"],"corresponding_institution_ids":["https://openalex.org/I4210134512"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.11807945,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"33","issue":null,"first_page":"145","last_page":"151"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9918000102043152,"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":0.9861999750137329,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8315464854240417},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7943556904792786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7831631898880005},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6782487034797668},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6519510746002197},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.6187351942062378},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5414214134216309},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5315523147583008},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5063408613204956},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4957181215286255},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4753784239292145},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.4515506327152252},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.39939430356025696},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3915390968322754},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09228318929672241},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07578986883163452}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8315464854240417},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7943556904792786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7831631898880005},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6782487034797668},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6519510746002197},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.6187351942062378},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5414214134216309},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5315523147583008},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5063408613204956},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4957181215286255},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4753784239292145},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.4515506327152252},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.39939430356025696},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3915390968322754},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09228318929672241},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07578986883163452},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/avss.2016.7738065","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss.2016.7738065","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5799999833106995,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W173906397","https://openalex.org/W182940129","https://openalex.org/W1674866864","https://openalex.org/W1925418734","https://openalex.org/W1971168232","https://openalex.org/W1985337547","https://openalex.org/W2000326692","https://openalex.org/W2002406878","https://openalex.org/W2003060733","https://openalex.org/W2016075127","https://openalex.org/W2024029849","https://openalex.org/W2070019681","https://openalex.org/W2089961441","https://openalex.org/W2098854771","https://openalex.org/W2098941887","https://openalex.org/W2099350109","https://openalex.org/W2105403175","https://openalex.org/W2109579504","https://openalex.org/W2111849182","https://openalex.org/W2113577207","https://openalex.org/W2118246710","https://openalex.org/W2118877769","https://openalex.org/W2139047213","https://openalex.org/W2163532725","https://openalex.org/W2165037244","https://openalex.org/W2165737454","https://openalex.org/W2168802423","https://openalex.org/W2293873019","https://openalex.org/W2405787592","https://openalex.org/W2541822344","https://openalex.org/W2543696449","https://openalex.org/W2753461371","https://openalex.org/W3100092831","https://openalex.org/W6674795655","https://openalex.org/W6675521158","https://openalex.org/W6676636569","https://openalex.org/W6677548441","https://openalex.org/W6684332838"],"related_works":["https://openalex.org/W4285411112","https://openalex.org/W2085033728","https://openalex.org/W2171299904","https://openalex.org/W2922442631","https://openalex.org/W2053596378","https://openalex.org/W2168523118","https://openalex.org/W2073639911","https://openalex.org/W2971551846","https://openalex.org/W2965594636","https://openalex.org/W2912550626"],"abstract_inverted_index":{"Drift":[0],"is":[1,93],"the":[2,17,20,32,35,64,86,109],"most":[3],"difficult":[4],"issue":[5],"in":[6,28,118],"object":[7,88],"visual":[8],"tracking":[9,42,75],"based":[10],"on":[11,85,127],"framework":[12],"of":[13,34],"\u201ctracking-by-detection\u201d.":[14],"Due":[15],"to":[16,25,72],"self-taught":[18],"learning,":[19],"mis-aligned":[21],"samples":[22],"are":[23,82],"potentially":[24],"be":[26],"incorporated":[27],"learning":[29],"and":[30,98,121],"degrade":[31],"discrimination":[33],"tracker.":[36],"This":[37],"paper":[38],"proposes":[39],"a":[40,53,59,96],"new":[41],"approach":[43],"that":[44,108],"resolves":[45],"this":[46],"problem":[47],"by":[48,95,102],"three":[49,110],"multi-level":[50],"collaborative":[51],"components:":[52],"high-level":[54],"global":[55],"appearance":[56],"tracker":[57,116],"provides":[58],"basic":[60],"prediction,":[61],"upon":[62],"which":[63,92],"structure":[65],"preserved":[66],"low-level":[67],"local":[68,80],"patches":[69,81],"matching":[70],"helps":[71],"guarantee":[73],"precise":[74],"with":[76],"minimized":[77],"drift.":[78],"Those":[79],"deliberately":[83],"deployed":[84],"foreground":[87],"via":[89],"foreground/background":[90],"segmentation,":[91],"realized":[94],"simple":[97],"efficient":[99],"classifier":[100],"trained":[101],"super-pixel":[103],"segments.":[104],"Experimental":[105],"results":[106],"show":[107],"closely":[111],"collaborated":[112],"components":[113],"enable":[114],"our":[115],"runs":[117],"real":[119],"time":[120],"performs":[122],"favourably":[123],"against":[124],"state-of-the-art":[125],"approaches":[126],"challenging":[128],"benchmark":[129],"sequences.":[130]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}