{"id":"https://openalex.org/W2516388125","doi":"https://doi.org/10.1109/icip.2016.7532653","title":"Saliency prior context model for visual tracking","display_name":"Saliency prior context model for visual tracking","publication_year":2016,"publication_date":"2016-08-17","ids":{"openalex":"https://openalex.org/W2516388125","doi":"https://doi.org/10.1109/icip.2016.7532653","mag":"2516388125"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2016.7532653","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532653","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 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/A5103175478","display_name":"Cong Ma","orcid":"https://orcid.org/0000-0003-4300-2754"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Cong Ma","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100298934","display_name":"Zhenjiang Miao","orcid":"https://orcid.org/0000-0001-8032-5769"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenjiang Miao","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, China"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100363146","display_name":"Xiao\u2013Ping Zhang","orcid":"https://orcid.org/0000-0001-5241-0069"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xiao-Ping Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, Toronto, Canada","institution_ids":["https://openalex.org/I530967"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5103175478"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.501,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.72549382,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1724","last_page":"1728"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9962999820709229,"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":0.9922999739646912,"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":0.7825376987457275},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.705893337726593},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6064822673797607},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6011921167373657},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.5243090391159058},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.514735221862793},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5070905685424805},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5054655075073242},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.4994947910308838},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4512485861778259},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4495122730731964},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3300588130950928},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.30554941296577454}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7825376987457275},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.705893337726593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6064822673797607},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6011921167373657},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.5243090391159058},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.514735221862793},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5070905685424805},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5054655075073242},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.4994947910308838},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4512485861778259},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4495122730731964},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3300588130950928},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30554941296577454},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2016.7532653","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2016.7532653","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 Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W29474918","https://openalex.org/W161114242","https://openalex.org/W1513768190","https://openalex.org/W1969459232","https://openalex.org/W1988689205","https://openalex.org/W2001785244","https://openalex.org/W2023640470","https://openalex.org/W2051985940","https://openalex.org/W2060814785","https://openalex.org/W2089961441","https://openalex.org/W2090748362","https://openalex.org/W2098941887","https://openalex.org/W2110116545","https://openalex.org/W2110546722","https://openalex.org/W2139047213","https://openalex.org/W2146103513","https://openalex.org/W2147339212","https://openalex.org/W2162383208","https://openalex.org/W2165037244","https://openalex.org/W2295767834","https://openalex.org/W6601205835","https://openalex.org/W6606595081","https://openalex.org/W6647421144"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2353836703"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,87],"present":[4],"a":[5,13,22,32],"new":[6,14,115,121],"FFT-based":[7],"visual":[8],"tracking":[9,27,116],"algorithm":[10,117],"based":[11,77,118],"on":[12,78,119],"model":[15,126],"for":[16],"computing":[17],"prior":[18,57,98,123],"context":[19,48,54,124],"distribution":[20,58],"via":[21],"spectral":[23,94],"saliency":[24,91,122],"approach.":[25],"The":[26],"problem":[28],"is":[29,64],"formulated":[30],"under":[31],"Bayesian":[33],"framework":[34],"where":[35],"the":[36,40,43,53,56,60,82,111,114,120],"statistical":[37],"relationships":[38],"between":[39],"features":[41,92],"of":[42,59,75,81,113],"target":[44,62,83],"and":[45,84],"its":[46,85],"spatio-temporal":[47],"are":[49],"modeled.":[50],"When":[51],"building":[52],"model,":[55],"possible":[61],"position":[63],"an":[65],"important":[66],"part":[67],"worth":[68],"studying.":[69],"To":[70],"deal":[71],"with":[72,131,138],"various":[73],"cases":[74],"distributions":[76],"different":[79],"attributes":[80],"context,":[86],"exploit":[88],"low":[89],"level":[90],"by":[93,107],"analysis":[95],"to":[96,102],"compute":[97],"distribution,":[99],"not":[100],"limited":[101],"center-surround":[103],"weights.":[104],"We":[105],"show":[106],"extensive":[108],"experiments":[109],"that":[110],"performance":[112,136],"(SPC)":[125],"achieves":[127],"real-time":[128],"computation":[129],"efficiency":[130],"overall":[132],"best":[133],"location":[134],"accuracy":[135],"compared":[137],"other":[139],"state-of-the-art":[140],"methods.":[141]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}