{"id":"https://openalex.org/W2793747070","doi":"https://doi.org/10.1109/icip.2017.8297100","title":"Context multi-task visual object tracking via guided filter","display_name":"Context multi-task visual object tracking via guided filter","publication_year":2017,"publication_date":"2017-09-01","ids":{"openalex":"https://openalex.org/W2793747070","doi":"https://doi.org/10.1109/icip.2017.8297100","mag":"2793747070"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2017.8297100","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297100","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":"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/A5100424354","display_name":"Yong Wang","orcid":"https://orcid.org/0000-0002-0578-8023"},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Yong Wang","raw_affiliation_strings":["School of Electrical and Computer Engineering, University of Ottawa"],"affiliations":[{"raw_affiliation_string":"School of Electrical and Computer Engineering, University of Ottawa","institution_ids":["https://openalex.org/I153718931"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101932698","display_name":"Xinbin Luo","orcid":"https://orcid.org/0000-0003-0852-7598"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinbin Luo","raw_affiliation_strings":["School of Electronic information and Electrical Engineering, Shanghai Jiao Tong University"],"affiliations":[{"raw_affiliation_string":"School of Electronic information and Electrical Engineering, Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002391274","display_name":"Shiqiang Hu","orcid":"https://orcid.org/0000-0002-9362-4642"},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiqiang Hu","raw_affiliation_strings":["School of Aeronautics and Astronautics, Shanghai Jiao Tong University"],"affiliations":[{"raw_affiliation_string":"School of Aeronautics and Astronautics, Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100424354"],"corresponding_institution_ids":["https://openalex.org/I153718931"],"apc_list":null,"apc_paid":null,"fwci":0.182,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6024924,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"33","issue":null,"first_page":"4332","last_page":"4336"},"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":0.996999979019165,"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":0.9922000169754028,"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/computer-science","display_name":"Computer science","score":0.7451363801956177},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7225478887557983},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6591900587081909},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6512863636016846},{"id":"https://openalex.org/keywords/bittorrent-tracker","display_name":"BitTorrent tracker","score":0.6174299716949463},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.6128231883049011},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5627378821372986},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5478160977363586},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.5456297397613525},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5342390537261963},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5217141509056091},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5018351078033447},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.4816414415836334},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.472507119178772},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.466324120759964},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33921363949775696},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.33132338523864746},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22348397970199585}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7451363801956177},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7225478887557983},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6591900587081909},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6512863636016846},{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.6174299716949463},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.6128231883049011},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5627378821372986},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5478160977363586},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.5456297397613525},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5342390537261963},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5217141509056091},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5018351078033447},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.4816414415836334},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.472507119178772},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.466324120759964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33921363949775696},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.33132338523864746},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22348397970199585},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2017.8297100","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2017.8297100","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":[{"score":0.7200000286102295,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1649469042","https://openalex.org/W1965791900","https://openalex.org/W2009243364","https://openalex.org/W2060814785","https://openalex.org/W2063273536","https://openalex.org/W2083616304","https://openalex.org/W2101886560","https://openalex.org/W2109579504","https://openalex.org/W2113577207","https://openalex.org/W2125188192","https://openalex.org/W2135562430","https://openalex.org/W2139047213","https://openalex.org/W2141364309","https://openalex.org/W2158917775","https://openalex.org/W2159213092","https://openalex.org/W2164278908","https://openalex.org/W2165037244","https://openalex.org/W2186094539","https://openalex.org/W2293417746","https://openalex.org/W2296411003","https://openalex.org/W4292363360","https://openalex.org/W6665839387","https://openalex.org/W6683636472","https://openalex.org/W6684332838"],"related_works":["https://openalex.org/W2062103941","https://openalex.org/W4384788979","https://openalex.org/W2511178891","https://openalex.org/W2126676984","https://openalex.org/W178060743","https://openalex.org/W2909390414","https://openalex.org/W2954509079","https://openalex.org/W3104472694","https://openalex.org/W2145247614","https://openalex.org/W2141888607"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"formulate":[4],"particle":[5],"filter":[6,67],"based":[7],"tracking":[8],"as":[9,26,37,45],"a":[10,111],"multi-task":[11,81],"sparse":[12,82],"learning":[13,83,95],"problem":[14,96],"that":[15,109,125,139],"exploits":[16],"context":[17,22,78,91],"information.":[18,79,92],"The":[19,65,93],"target":[20,73,89],"and":[21,48,62,90,133],"information":[23],"which":[24],"modeled":[25],"linear":[27],"combinations":[28],"of":[29,105,113],"principal":[30],"component":[31],"analysis":[32],"(PCA)":[33],"basis":[34],"is":[35,84,97],"formed":[36],"dictionary":[38,43],"templates.":[39],"We":[40,117],"treat":[41],"the":[42,46,49,56,59,72,88],"templates":[44],"guidance":[47,60],"incoming":[50],"candidates":[51,76],"are":[52],"filtered":[53],"depending":[54],"on":[55,121],"similarity":[57],"between":[58],"image":[61],"each":[63],"input.":[64],"guided":[66],"can":[68],"help":[69],"to":[70,86],"distinguish":[71],"from":[74],"numerous":[75],"via":[77],"Then":[80],"employed":[85],"learn":[87],"proposed":[94],"efficiently":[98],"solved":[99],"using":[100],"an":[101],"alternating":[102],"direction":[103],"method":[104,108],"multipliers":[106],"(ADMM)":[107],"yield":[110],"sequence":[112],"closed":[114],"form":[115],"updates.":[116],"test":[118],"our":[119,140],"tracker":[120,141],"challenging":[122],"benchmark":[123],"sequences":[124],"involve":[126],"drastic":[127],"illumination":[128],"changes,":[129],"large":[130],"pose":[131],"variations,":[132],"heavy":[134],"occlusion.":[135],"Experimental":[136],"results":[137],"show":[138],"consistently":[142],"outperforms":[143],"state-of-the-art":[144],"trackers.":[145]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}