{"id":"https://openalex.org/W2789819619","doi":"https://doi.org/10.1109/globalsip.2017.8308626","title":"Selective parts-based tracking through occlusions","display_name":"Selective parts-based tracking through occlusions","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2789819619","doi":"https://doi.org/10.1109/globalsip.2017.8308626","mag":"2789819619"},"language":"en","primary_location":{"id":"doi:10.1109/globalsip.2017.8308626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2017.8308626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5010341160","display_name":"Maria Cornacchia","orcid":null},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Maria Cornacchia","raw_affiliation_strings":["Syracuse University, Syracuse, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, USA","institution_ids":["https://openalex.org/I70983195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004337702","display_name":"Senem Velipasalar","orcid":"https://orcid.org/0000-0002-1430-1555"},"institutions":[{"id":"https://openalex.org/I70983195","display_name":"Syracuse University","ror":"https://ror.org/025r5qe02","country_code":"US","type":"education","lineage":["https://openalex.org/I70983195"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Senem Velipasalar","raw_affiliation_strings":["Syracuse University, Syracuse, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Syracuse University, Syracuse, USA","institution_ids":["https://openalex.org/I70983195"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I70983195"],"apc_list":null,"apc_paid":null,"fwci":0.2388,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.64204746,"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":"171","last_page":"175"},"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":0.9937999844551086,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9915000200271606,"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":0.8253092765808105},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7697364091873169},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7596518993377686},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7568870782852173},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.7277897596359253},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6687484383583069},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.5304054021835327},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5223489999771118},{"id":"https://openalex.org/keywords/motion-blur","display_name":"Motion blur","score":0.5169984102249146},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.49606308341026306},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.49322956800460815},{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.4727246165275574},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.42373955249786377},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.37542492151260376},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1867760419845581},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.09402942657470703},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08587488532066345}],"concepts":[{"id":"https://openalex.org/C57501372","wikidata":"https://www.wikidata.org/wiki/Q2021268","display_name":"BitTorrent tracker","level":3,"score":0.8253092765808105},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7697364091873169},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7596518993377686},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7568870782852173},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.7277897596359253},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6687484383583069},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.5304054021835327},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5223489999771118},{"id":"https://openalex.org/C2777708103","wikidata":"https://www.wikidata.org/wiki/Q852589","display_name":"Motion blur","level":3,"score":0.5169984102249146},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.49606308341026306},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.49322956800460815},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.4727246165275574},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.42373955249786377},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.37542492151260376},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1867760419845581},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.09402942657470703},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08587488532066345},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/globalsip.2017.8308626","is_oa":false,"landing_page_url":"https://doi.org/10.1109/globalsip.2017.8308626","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","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":16,"referenced_works":["https://openalex.org/W147001025","https://openalex.org/W818325216","https://openalex.org/W1857884451","https://openalex.org/W1908905119","https://openalex.org/W1997121481","https://openalex.org/W2089961441","https://openalex.org/W2100916003","https://openalex.org/W2101194540","https://openalex.org/W2158592639","https://openalex.org/W2165846633","https://openalex.org/W2214352687","https://openalex.org/W2342770894","https://openalex.org/W2503931548","https://openalex.org/W2951157758","https://openalex.org/W6623108133","https://openalex.org/W6649598916"],"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/W2070920257","https://openalex.org/W1765993298"],"abstract_inverted_index":{"Visual":[0],"tracking":[1,70,88],"is":[2],"a":[3,18,32,51,63,105],"difficult":[4],"task":[5],"due":[6],"to":[7,34,71,77,113,145],"numerous":[8,38],"scale,":[9],"occlusion,":[10],"motion":[11],"blur,":[12],"and":[13,68,108,141,149],"other":[14],"deformation":[15],"changes":[16,40],"through-out":[17],"video":[19],"sequence.":[20],"While":[21],"correlation":[22,56],"filter":[23],"trackers":[24],"have":[25],"recently":[26],"shown":[27],"promise,":[28],"it":[29],"still":[30],"remains":[31],"challenge":[33],"account":[35],"for":[36,124],"the":[37,66,81],"different":[39,129],"of":[41,65],"an":[42,115],"object":[43],"during":[44],"tracking.":[45],"In":[46,75],"this":[47],"paper,":[48],"we":[49,99],"propose":[50],"selective":[52],"parts-based":[53,147],"approach,":[54],"using":[55],"filters,":[57],"that":[58],"makes":[59],"choices":[60],"based":[61],"on":[62,110],"consensus":[64],"parts":[67,94],"global":[69],"track":[72],"through":[73],"occlusions.":[74],"contrast":[76],"existing":[78],"part-based":[79],"methods,":[80],"proposed":[82,120],"method":[83],"does":[84],"not":[85],"dilute":[86],"accurate":[87,116],"by":[89],"averaging":[90],"results":[91],"over":[92],"multiple":[93],"at":[95],"every":[96],"frame.":[97],"Instead,":[98],"only":[100],"make":[101],"location":[102],"corrections":[103,112],"when":[104],"part":[106],"diverges":[107],"rely":[109],"these":[111],"maintain":[114],"appearance":[117],"model.":[118],"The":[119],"approach":[121,134],"was":[122],"evaluated":[123],"scenarios":[125],"obtained":[126],"from":[127],"two":[128],"challenging":[130],"benchmark":[131],"datasets.":[132],"Our":[133],"has":[135,150],"resulted":[136],"in":[137,154],"better":[138,152],"overall":[139],"precision":[140],"success":[142],"rates":[143],"compared":[144],"recent":[146],"approaches,":[148],"performed":[151],"especially":[153],"occlusion":[155],"scenarios.":[156]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}