{"id":"https://openalex.org/W2524642387","doi":"https://doi.org/10.1109/icmew.2016.7574746","title":"How does human interest modeling help in computer vision: Tracking-by-saliency in unconstrained social videos","display_name":"How does human interest modeling help in computer vision: Tracking-by-saliency in unconstrained social videos","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2524642387","doi":"https://doi.org/10.1109/icmew.2016.7574746","mag":"2524642387"},"language":"en","primary_location":{"id":"doi:10.1109/icmew.2016.7574746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2016.7574746","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 &amp; Expo Workshops (ICMEW)","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/A5100364068","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0001-9690-7026"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042406159","display_name":"Tao Zhuo","orcid":"https://orcid.org/0000-0001-8860-1887"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tao Zhuo","raw_affiliation_strings":["School of Computing, National University of Singapore, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Computing, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001764217","display_name":"Kangli Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kangli Chen","raw_affiliation_strings":["School of Computer Science, Northwestern Polytechnical University, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Northwestern Polytechnical University, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029470409","display_name":"Wei Huang","orcid":"https://orcid.org/0000-0003-2700-4928"},"institutions":[{"id":"https://openalex.org/I141649914","display_name":"Nanchang University","ror":"https://ror.org/042v6xz23","country_code":"CN","type":"education","lineage":["https://openalex.org/I141649914"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Huang","raw_affiliation_strings":["School of Information Engineering, Nanchang University, China"],"affiliations":[{"raw_affiliation_string":"School of Information Engineering, Nanchang University, China","institution_ids":["https://openalex.org/I141649914"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100364068"],"corresponding_institution_ids":["https://openalex.org/I17145004"],"apc_list":null,"apc_paid":null,"fwci":0.334,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.66573246,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"36","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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9991999864578247,"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/T11165","display_name":"Image and Video Quality Assessment","score":0.9833999872207642,"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":0.8354772329330444},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7661705017089844},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.74791020154953},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7134836316108704},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6513065695762634},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6511198282241821},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6403051614761353},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5689734816551208},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5593539476394653},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.5418345332145691},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4656698405742645},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4346475899219513},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.4287915825843811},{"id":"https://openalex.org/keywords/region-of-interest","display_name":"Region of interest","score":0.41100814938545227},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3232889175415039}],"concepts":[{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.8354772329330444},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7661705017089844},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.74791020154953},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7134836316108704},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6513065695762634},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6511198282241821},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6403051614761353},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5689734816551208},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5593539476394653},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.5418345332145691},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4656698405742645},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4346475899219513},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.4287915825843811},{"id":"https://openalex.org/C19609008","wikidata":"https://www.wikidata.org/wiki/Q2138203","display_name":"Region of interest","level":2,"score":0.41100814938545227},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3232889175415039},{"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/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"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},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","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/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmew.2016.7574746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmew.2016.7574746","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 &amp; Expo Workshops (ICMEW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6800000071525574,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W29474918","https://openalex.org/W1560724230","https://openalex.org/W1915599933","https://openalex.org/W1954128991","https://openalex.org/W1977709942","https://openalex.org/W1988689205","https://openalex.org/W2003060733","https://openalex.org/W2027021064","https://openalex.org/W2037954058","https://openalex.org/W2059673930","https://openalex.org/W2087570216","https://openalex.org/W2097878628","https://openalex.org/W2098941887","https://openalex.org/W2105610271","https://openalex.org/W2105644991","https://openalex.org/W2109579504","https://openalex.org/W2124211486","https://openalex.org/W2132105090","https://openalex.org/W2140045824","https://openalex.org/W2154889144","https://openalex.org/W2155709634","https://openalex.org/W2156547441","https://openalex.org/W2161969291","https://openalex.org/W2162919312","https://openalex.org/W2165037244","https://openalex.org/W2211996548","https://openalex.org/W3144619878","https://openalex.org/W3195149063","https://openalex.org/W4239147634","https://openalex.org/W4243833160","https://openalex.org/W6640615796","https://openalex.org/W6651148544","https://openalex.org/W6675760969","https://openalex.org/W6678512814","https://openalex.org/W6680820227","https://openalex.org/W6682652761","https://openalex.org/W6806823677","https://openalex.org/W7058071925"],"related_works":["https://openalex.org/W4384788979","https://openalex.org/W2511178891","https://openalex.org/W178060743","https://openalex.org/W2909390414","https://openalex.org/W2126676984","https://openalex.org/W2954509079","https://openalex.org/W2141888607","https://openalex.org/W2753886513","https://openalex.org/W2061955552","https://openalex.org/W2070920257"],"abstract_inverted_index":{"Sample":[0],"quality":[1],"plays":[2],"an":[3,54],"important":[4],"role":[5],"in":[6,40,43],"tracking-by-learning":[7],"strategies,":[8],"but":[9],"the":[10,33,64,90,96,110,114,119],"reliable":[11],"online":[12,67],"samples":[13,68],"are":[14],"hard":[15],"to":[16,20,61],"be":[17],"obtained":[18],"due":[19],"challenges":[21],"of":[22,35,66,113],"variational":[23],"environments.":[24],"By":[25],"modeling":[26],"how":[27],"human":[28],"visual":[29],"interest":[30],"actively":[31],"guiding":[32],"seek":[34],"salient":[36],"regions":[37],"and":[38,99],"movements":[39],"video":[41],"sequences,":[42],"this":[44],"paper,":[45],"a":[46,71,84],"compositional":[47],"tracking":[48,92,116],"strategy":[49],"is":[50,59,76],"proposed":[51,78,91,115],"based":[52,73],"on":[53,104],"integrated":[55],"saliency":[56],"map,":[57],"which":[58],"able":[60],"accurately":[62],"guide":[63],"process":[65],"generation.":[69],"Meanwhile,":[70],"segmentation":[72],"refinement":[74],"method":[75],"also":[77],"for":[79],"effective":[80],"model":[81],"updating.":[82],"With":[83],"high":[85],"performance":[86],"kernelized":[87],"correlation":[88],"filter,":[89],"can":[93],"efficiently":[94],"handle":[95],"complex":[97],"intrinsic":[98],"extrinsic":[100],"appearance":[101],"changes.":[102],"Experiments":[103],"challenging":[105],"benchmark":[106],"databases":[107],"demonstrate":[108],"that":[109],"robust":[111],"accuracy":[112],"against":[117],"with":[118],"other":[120],"state-of-the-art":[121],"trackers.":[122]},"counts_by_year":[{"year":2019,"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"}
