{"id":"https://openalex.org/W2400549852","doi":null,"title":"SVM classification of moving objects tracked by Kalman filter and Hungarian method.","display_name":"SVM classification of moving objects tracked by Kalman filter and Hungarian method.","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2400549852","mag":"2400549852"},"language":"en","primary_location":{"id":"mag:2400549852","doi":null,"is_oa":false,"landing_page_url":"http://ceur-ws.org/Vol-1391/40-CR.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306506385","display_name":"CLEF (Working Notes)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"CLEF (Working Notes)","raw_type":null},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5076485961","display_name":"G\u00e1bor Sz\u00fccs","orcid":"https://orcid.org/0000-0002-5781-1088","observed_orcids":["https://orcid.org/0000-0002-5781-1088"]},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"G\u00e1bor Sz\u0171cs","raw_affiliation_strings":["Budapest University of Technology and Economics"],"raw_orcid":"https://orcid.org/0000-0002-5781-1088","affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics","institution_ids":["https://openalex.org/I29770179"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046406057","display_name":"D\u00e1vid Papp","orcid":"https://orcid.org/0000-0002-8814-2745","observed_orcids":["https://orcid.org/0000-0002-8814-2745"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"D\u00e1vid Papp","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-8814-2745","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5066850940","display_name":"D\u00e1niel Lovas","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I29770179","display_name":"Budapest University of Technology and Economics","ror":"https://ror.org/02w42ss30","country_code":"HU","type":"education","lineage":["https://openalex.org/I29770179"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"D\u00e1niel Lovas","raw_affiliation_strings":["Budapest University of Technology and Economics"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Budapest University of Technology and Economics","institution_ids":["https://openalex.org/I29770179"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4013,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.63157895,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9437999725341797,"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":0.9437999725341797,"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.0406000018119812,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.002400000113993883,"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/fish-species-identification","display_name":"fish identification","score":0.7310000061988831},{"id":"https://openalex.org/keywords/underwater-video","display_name":"underwater video","score":0.9330000281333923},{"id":"https://openalex.org/keywords/object-tracking","display_name":"object tracking","score":0.6499999761581421},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.796999990940094},{"id":"https://openalex.org/keywords/hungarian-algorithm","display_name":"Hungarian algorithm","score":0.7850000262260437},{"id":"https://openalex.org/keywords/object-matching","display_name":"object matching","score":0.6290000081062317},{"id":"https://openalex.org/keywords/background-subtraction","display_name":"background subtraction","score":0.9039999842643738},{"id":"https://openalex.org/keywords/morphological-method","display_name":"morphological method","score":0.5590000152587891},{"id":"https://openalex.org/keywords/bounding-box-detection","display_name":"bounding box detection","score":0.6039999723434448},{"id":"https://openalex.org/keywords/surf-descriptors","display_name":"SURF descriptors","score":0.777999997138977},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.75}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7716048955917358},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6682141423225403},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6651228070259094},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6331652998924255},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6193984150886536},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5674993991851807},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.443536639213562}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:2400549852","doi":null,"is_oa":false,"landing_page_url":"http://ceur-ws.org/Vol-1391/40-CR.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306506385","display_name":"CLEF (Working Notes)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"CLEF (Working Notes)","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W401671674","https://openalex.org/W1573546770","https://openalex.org/W1677409904","https://openalex.org/W2065429801","https://openalex.org/W2087347434","https://openalex.org/W2105934661","https://openalex.org/W2124386111","https://openalex.org/W2126045901","https://openalex.org/W2127218421","https://openalex.org/W2151103935","https://openalex.org/W2162915993","https://openalex.org/W2222512263","https://openalex.org/W2915431448","https://openalex.org/W4239510810"],"related_works":["https://openalex.org/W2566800024","https://openalex.org/W2066854622","https://openalex.org/W3036099705","https://openalex.org/W319006245","https://openalex.org/W2395836822","https://openalex.org/W2293210054","https://openalex.org/W3000061099","https://openalex.org/W2329880793","https://openalex.org/W2955871274","https://openalex.org/W2972849548"],"abstract_inverted_index":{"Fishery":[0],"video":[1,41,169],"data":[2],"often":[3],"require":[4],"laborious":[5],"visual":[6],"analysis,":[7],"therefore":[8],"a":[9,29,147],"video-based":[10],"fish":[11,22,163],"identification":[12],"challenge":[13],"is":[14],"announced":[15],"in":[16,39,91,137,165],"the":[17,50,77,89,109,133,142,153,158,162,166],"LifeCLEF":[18],"campaign":[19],"for":[20,103,156],"automatic":[21],"categorization":[23],"and":[24,35,57,60,150,171],"enumeration.":[25],"We":[26,71,99,107,160],"have":[27],"elaborated":[28],"complex":[30],"system":[31],"to":[32,75,87],"detect,":[33],"classify":[34],"track":[36,76],"objects":[37,90],"(fishes)":[38],"underwater":[40],"by":[42,177],"examining":[43],"each":[44,138],"image":[45],"frame":[46],"of":[47,96],"it.":[48],"For":[49,140],"detection":[51],"process":[52],"we":[53,145,151],"used":[54,72,100,125,152],"background":[55],"subtraction":[56],"morphologic":[58],"methods,":[59],"then":[61],"our":[62,172],"solution":[63],"calculated":[64],"bounding":[65],"boxes":[66],"based":[67,131],"on":[68,132],"object":[69],"contours.":[70],"Kalman":[73],"filter":[74],"moving":[78],"objects,":[79],"but":[80],"an":[81,116],"additional":[82],"matching":[83,105],"method":[84,102],"was":[85],"required":[86],"pair":[88],"consecutive":[92],"time":[93],"periods":[94],"because":[95],"many":[97],"fishes.":[98],"Hungarian":[101],"this":[104],"problem.":[106],"categorized":[108],"detected":[110],"fishes":[111],"with":[112],"C-SVC":[113,143],"classifier,":[114],"as":[115],"advanced":[117],"SVM":[118],"(Support":[119],"Vector":[120],"Machine)":[121],"classifier.":[122,159],"The":[123],"classifier":[124,144],"high":[126],"level":[127],"descriptors,":[128],"which":[129],"are":[130],"extracted":[134],"SURF":[135],"vectors":[136],"object.":[139],"optimization":[141],"conducted":[146],"preliminary":[148],"test,":[149],"best":[154],"parameters":[155],"teaching":[157],"predicted":[161],"species":[164],"official":[167],"test":[168],"set,":[170],"predictions":[173],"were":[174],"evaluated":[175],"officially":[176],"NCS":[178],"(Normalized":[179],"Counting":[180],"Score).":[181]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
