{"id":"https://openalex.org/W2544108364","doi":"https://doi.org/10.1109/acpr.2011.6166607","title":"A fast stereo-based multi-person tracking using an approximated likelihood map for overlapping silhouette templates","display_name":"A fast stereo-based multi-person tracking using an approximated likelihood map for overlapping silhouette templates","publication_year":2011,"publication_date":"2011-11-01","ids":{"openalex":"https://openalex.org/W2544108364","doi":"https://doi.org/10.1109/acpr.2011.6166607","mag":"2544108364"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2011.6166607","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2011.6166607","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The First Asian Conference on Pattern Recognition","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/A5048251724","display_name":"Junji Satake","orcid":null},"institutions":[{"id":"https://openalex.org/I136259955","display_name":"Toyohashi University of Technology","ror":"https://ror.org/04ezg6d83","country_code":"JP","type":"education","lineage":["https://openalex.org/I136259955"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Junji Satake","raw_affiliation_strings":["Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan","institution_ids":["https://openalex.org/I136259955"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071725508","display_name":"Jun Miura","orcid":"https://orcid.org/0000-0003-0153-2570"},"institutions":[{"id":"https://openalex.org/I136259955","display_name":"Toyohashi University of Technology","ror":"https://ror.org/04ezg6d83","country_code":"JP","type":"education","lineage":["https://openalex.org/I136259955"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Jun Miura","raw_affiliation_strings":["Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi, Japan","institution_ids":["https://openalex.org/I136259955"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5048251724"],"corresponding_institution_ids":["https://openalex.org/I136259955"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34028524,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"392","last_page":"396"},"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":0.9976999759674072,"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":0.9914000034332275,"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/silhouette","display_name":"Silhouette","score":0.9520124197006226},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7685113549232483},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.7166982889175415},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7126455903053284},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7042427659034729},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6419492959976196},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.619776725769043},{"id":"https://openalex.org/keywords/template","display_name":"Template","score":0.544026255607605},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49136391282081604},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.43176013231277466},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.3892776072025299},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3203828036785126},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.20096060633659363}],"concepts":[{"id":"https://openalex.org/C58103923","wikidata":"https://www.wikidata.org/wiki/Q2286025","display_name":"Silhouette","level":2,"score":0.9520124197006226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7685113549232483},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.7166982889175415},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7126455903053284},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7042427659034729},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6419492959976196},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.619776725769043},{"id":"https://openalex.org/C82714645","wikidata":"https://www.wikidata.org/wiki/Q438331","display_name":"Template","level":2,"score":0.544026255607605},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49136391282081604},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.43176013231277466},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.3892776072025299},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3203828036785126},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.20096060633659363},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","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},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2011.6166607","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2011.6166607","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The First Asian Conference on Pattern Recognition","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":21,"referenced_works":["https://openalex.org/W80618319","https://openalex.org/W202219663","https://openalex.org/W1499578337","https://openalex.org/W1751816758","https://openalex.org/W1992825118","https://openalex.org/W2037399936","https://openalex.org/W2063456401","https://openalex.org/W2096837160","https://openalex.org/W2098699644","https://openalex.org/W2109666745","https://openalex.org/W2110370029","https://openalex.org/W2148725385","https://openalex.org/W2150298366","https://openalex.org/W2150978447","https://openalex.org/W2158052077","https://openalex.org/W2161969291","https://openalex.org/W2169566837","https://openalex.org/W6603199885","https://openalex.org/W6608230807","https://openalex.org/W6629966464","https://openalex.org/W6637940311"],"related_works":["https://openalex.org/W1622964048","https://openalex.org/W30315714","https://openalex.org/W1906975550","https://openalex.org/W1965274140","https://openalex.org/W779885325","https://openalex.org/W2150972844","https://openalex.org/W2393615320","https://openalex.org/W3110435694","https://openalex.org/W2001760863","https://openalex.org/W2185145003"],"abstract_inverted_index":{"This":[0],"paper":[1],"describes":[2],"a":[3,53,56,80],"method":[4,35,51,82,108],"of":[5,58,88],"tracking":[6,34,120],"multiple":[7],"persons":[8,43],"with":[9,72],"occlusions":[10],"using":[11,36,92],"stereo.":[12],"Many":[13],"previous":[14],"stereo-based":[15],"systems":[16],"track":[17],"each":[18,65],"person":[19],"separately":[20],"and":[21],"do":[22],"not":[23],"explicitly":[24],"handle":[25],"such":[26],"occlusions.":[27],"We":[28],"previously":[29],"developed":[30],"an":[31,93],"accurate,":[32],"stable":[33],"overlapping":[37],"silhouette":[38],"templates":[39,71],"which":[40,83],"considers":[41],"how":[42],"overlap":[44],"in":[45],"the":[46,50,73,86,106,113,119],"image.":[47,74],"However,":[48],"because":[49],"uses":[52],"particle":[54],"filter,":[55],"lot":[57],"processing":[59,114],"time":[60,115],"is":[61,109],"needed":[62],"for":[63],"estimating":[64],"particle's":[66],"likelihood":[67,95],"by":[68,91],"comparing":[69],"many":[70],"In":[75],"this":[76],"paper,":[77],"we":[78],"propose":[79],"new":[81],"can":[84],"decrease":[85],"number":[87],"image":[89],"comparison":[90],"approximated":[94],"map":[96],"based":[97],"on":[98],"kernel":[99],"density":[100],"estimation.":[101],"Experimental":[102],"results":[103],"show":[104],"that":[105],"proposed":[107],"able":[110],"to":[111],"reduce":[112],"greatly":[116],"without":[117],"dropping":[118],"performance.":[121]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
