{"id":"https://openalex.org/W2516868439","doi":"https://doi.org/10.1109/icme.2016.7552969","title":"Robust online visual tracking via a temporal ensemble framework","display_name":"Robust online visual tracking via a temporal ensemble framework","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2516868439","doi":"https://doi.org/10.1109/icme.2016.7552969","mag":"2516868439"},"language":"en","primary_location":{"id":"doi:10.1109/icme.2016.7552969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2016.7552969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia and Expo (ICME)","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/A5101829437","display_name":"Hao Guan","orcid":"https://orcid.org/0000-0002-9077-774X"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Guan","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003418019","display_name":"Xiangyang Xue","orcid":"https://orcid.org/0000-0002-4897-9209"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Xue","raw_affiliation_strings":["Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5101829437"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":0.167,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.56809371,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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":0.9934999942779541,"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/T11439","display_name":"Video Analysis and Summarization","score":0.9926000237464905,"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/bittorrent-tracker","display_name":"BitTorrent tracker","score":0.8794443607330322},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.7891496419906616},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7579014301300049},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7305784225463867},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"Ensemble learning","score":0.5652942657470703},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.5651895403862},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5480605959892273},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5404824018478394},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4852719008922577},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.46236374974250793},{"id":"https://openalex.org/keywords/tracking-system","display_name":"Tracking system","score":0.4423504173755646},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4326155185699463},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4250290095806122},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3775935769081116},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.358936607837677},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.07716014981269836}],"concepts":[{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.8794443607330322},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7891496419906616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7579014301300049},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7305784225463867},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.5652942657470703},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.5651895403862},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5480605959892273},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5404824018478394},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4852719008922577},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.46236374974250793},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.4423504173755646},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4326155185699463},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4250290095806122},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3775935769081116},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.358936607837677},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.07716014981269836},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme.2016.7552969","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme.2016.7552969","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1807914171","https://openalex.org/W2000326692","https://openalex.org/W2023640470","https://openalex.org/W2031489346","https://openalex.org/W2038943746","https://openalex.org/W2044986361","https://openalex.org/W2060814785","https://openalex.org/W2089961441","https://openalex.org/W2098941887","https://openalex.org/W2108215708","https://openalex.org/W2112314277","https://openalex.org/W2124211486","https://openalex.org/W2126302311","https://openalex.org/W2132105090","https://openalex.org/W2139047213","https://openalex.org/W2154889144","https://openalex.org/W2162383208","https://openalex.org/W2165037244","https://openalex.org/W2167089254","https://openalex.org/W2293873019","https://openalex.org/W4206196235","https://openalex.org/W4231340930","https://openalex.org/W6638399501","https://openalex.org/W6684274140","https://openalex.org/W6684332838"],"related_works":["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/W2141888607","https://openalex.org/W2753886513","https://openalex.org/W1765993298"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5,12,26,38,57],"robust":[6],"visual":[7],"tracking":[8,53,114,128],"method":[9,36,129],"based":[10,89],"on":[11,90,119],"temporal":[13,49,58,96],"ensemble":[14,59,77],"framework.":[15,60],"Different":[16],"from":[17],"conventional":[18],"ensemble-based":[19],"trackers,":[20],"which":[21,107],"combine":[22],"weak":[23],"classifiers":[24],"into":[25],"strong":[27],"one":[28],"using":[29],"AdBoost":[30],"in":[31,47,134],"spatial":[32],"fusion":[33,83],"manners,":[34],"our":[35],"adopts":[37],"powerful":[39],"and":[40,139],"efficient":[41],"tracker":[42],"integrated":[43],"with":[44],"its":[45],"snapshots":[46,88],"different":[48],"windows":[50],"of":[51,84,112,136],"online":[52,74],"process":[54],"to":[55],"construct":[56],"Specifically,":[61],"an":[62],"adaptive":[63],"correlation":[64],"filter":[65],"classifier":[66],"is":[67],"employed":[68],"as":[69],"the":[70,76,80,85,95,110,113,126],"base":[71,86],"tracker.":[72],"During":[73],"tracking,":[75],"model":[78],"determines":[79],"output":[81],"through":[82],"tracker's":[87],"their":[91],"response":[92],"scores.":[93],"By":[94],"ensemble,":[97],"accumulated":[98],"errors":[99],"caused":[100],"by":[101],"undesirable":[102],"update":[103],"can":[104],"be":[105],"corrected":[106],"greatly":[108],"improves":[109],"robustness":[111],"system.":[115],"Encouraging":[116],"experimental":[117],"results":[118],"challenging":[120],"benchmark":[121],"video":[122],"sequences":[123],"demonstrate":[124],"that":[125],"proposed":[127],"outperforms":[130],"several":[131],"state-of-the-art":[132],"trackers":[133],"terms":[135],"both":[137],"precision":[138],"robustness.":[140]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}