{"id":"https://openalex.org/W2897517286","doi":"https://doi.org/10.1145/3265845.3265846","title":"Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video","display_name":"Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video","publication_year":2018,"publication_date":"2018-10-19","ids":{"openalex":"https://openalex.org/W2897517286","doi":"https://doi.org/10.1145/3265845.3265846","mag":"2897517286"},"language":"en","primary_location":{"id":"doi:10.1145/3265845.3265846","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3265845.3265846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","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/A5049792063","display_name":"Morten B. Jensen","orcid":null},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Morten B. Jensen","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076290245","display_name":"Rikke Gade","orcid":"https://orcid.org/0000-0002-8016-2426"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Rikke Gade","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022176859","display_name":"Thomas B. Moeslund","orcid":"https://orcid.org/0000-0001-7584-5209"},"institutions":[{"id":"https://openalex.org/I891191580","display_name":"Aalborg University","ror":"https://ror.org/04m5j1k67","country_code":"DK","type":"education","lineage":["https://openalex.org/I891191580"]}],"countries":["DK"],"is_corresponding":false,"raw_author_name":"Thomas B. Moeslund","raw_affiliation_strings":["Aalborg University, Aalborg, Denmark"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aalborg University, Aalborg, Denmark","institution_ids":["https://openalex.org/I891191580"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I891191580"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"67","last_page":"73"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9990000128746033,"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":0.9990000128746033,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T11019","display_name":"Image Enhancement Techniques","score":0.9940999746322632,"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/occupancy","display_name":"Occupancy","score":0.8825935125350952},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7692404389381409},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.6624555587768555},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6057848930358887},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5544111132621765},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.524334728717804},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.4502236545085907},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43409180641174316},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.33496662974357605},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1134108304977417}],"concepts":[{"id":"https://openalex.org/C160331591","wikidata":"https://www.wikidata.org/wiki/Q7075743","display_name":"Occupancy","level":2,"score":0.8825935125350952},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7692404389381409},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.6624555587768555},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6057848930358887},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5544111132621765},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.524334728717804},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.4502236545085907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43409180641174316},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33496662974357605},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1134108304977417},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/3265845.3265846","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3265845.3265846","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st International Workshop on Multimedia Content Analysis in Sports","raw_type":"proceedings-article"},{"id":"pmh:oai:pure.atira.dk:publications/b4791aa8-cc1e-443f-bc58-e29f1642f7f7","is_oa":false,"landing_page_url":"https://vbn.aau.dk/da/publications/b4791aa8-cc1e-443f-bc58-e29f1642f7f7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401731","display_name":"VBN Forskningsportal (Aalborg Universitet)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I891191580","host_organization_name":"Aalborg University","host_organization_lineage":["https://openalex.org/I891191580"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Jensen , M B , Gade , R & Moeslund , T B 2018 , Swimming Pool Occupancy Analysis using Deep Learning on Low Quality Video . in ACM Multimedia Conference Workshops : First International Workshop on Multimedia Content Analysis in Sports . Association for Computing Machinery , pp. 67-73 , ACM Multimedia Conference Workshops , Seoul , Korea, Republic of , 22/10/2018 . https://doi.org/10.1145/3265845.3265846","raw_type":"contributionToPeriodical"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1562810981","https://openalex.org/W1833143043","https://openalex.org/W1976526581","https://openalex.org/W2088238109","https://openalex.org/W2122038369","https://openalex.org/W2148784764","https://openalex.org/W2151896334","https://openalex.org/W2158310867","https://openalex.org/W2167533884","https://openalex.org/W2210445161","https://openalex.org/W2565639579","https://openalex.org/W2583758901","https://openalex.org/W2585166381","https://openalex.org/W2963037989","https://openalex.org/W3106250896","https://openalex.org/W4234333737","https://openalex.org/W4252736875","https://openalex.org/W4298417552","https://openalex.org/W4300101671","https://openalex.org/W4407467710"],"related_works":["https://openalex.org/W4282043467","https://openalex.org/W2105697914","https://openalex.org/W2202433167","https://openalex.org/W3093197249","https://openalex.org/W1540010871","https://openalex.org/W3023979140","https://openalex.org/W3177545769","https://openalex.org/W2904068067","https://openalex.org/W4285822468","https://openalex.org/W4380075502"],"abstract_inverted_index":{"Automatically":[0],"creating":[1],"spatio-temporal":[2,61],"occupancy":[3,62],"analysis":[4],"of":[5,10,20,63,85,93,110,122],"public":[6],"swimming":[7,50,65],"pools":[8],"is":[9,71],"great":[11],"interest,":[12],"both":[13,123],"for":[14,25,58],"administrators":[15],"to":[16,27,73],"optimize":[17],"the":[18,60,120],"use":[19],"these":[21],"expensive":[22],"facilities,":[23],"and":[24],"users":[26],"schedule":[28],"their":[29],"activities":[30],"outside":[31],"peak":[32],"hours.":[33],"In":[34],"this":[35],"paper":[36],"we":[37,54],"apply":[38],"current":[39],"state-of-the-art":[40],"deep":[41],"learning":[42],"methods":[43],"within":[44],"human":[45],"detection":[46],"on":[47,104,115],"low":[48],"quality":[49],"pool":[51],"video.":[52],"Furthermore,":[53],"propose":[55],"a":[56,64,105],"method":[57],"analyzing":[59],"pool.":[66],"We":[67,117],"show":[68],"that":[69,119],"it":[70],"possible":[72],"precisely":[74],"detect":[75],"swimmers":[76],"in":[77,113],"very":[78],"challenging":[79],"conditions":[80],"by":[81],"obtaining":[82],"an":[83],"AUC":[84,92],"93.48":[86],"%":[87,95],"from":[88,98],"YOLOv2.":[89],"An":[90],"acceptable":[91],"79.29":[94],"was":[96],"obtained":[97],"Tiny-YOLO,":[99],"which":[100],"can":[101,125],"be":[102,126],"implemented":[103],"low-cost":[106],"embedded":[107],"system":[108],"capable":[109],"producing":[111],"results":[112],"real-time":[114],"site.":[116],"expect":[118],"performance":[121],"networks":[124],"improved":[127],"with":[128],"more":[129],"training":[130],"data.":[131]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}