{"id":"https://openalex.org/W4312876966","doi":"https://doi.org/10.1145/3556677.3556701","title":"Moving Object Tracking Method Based on SVM and Meanshift Tracking Algorithm","display_name":"Moving Object Tracking Method Based on SVM and Meanshift Tracking Algorithm","publication_year":2022,"publication_date":"2022-07-26","ids":{"openalex":"https://openalex.org/W4312876966","doi":"https://doi.org/10.1145/3556677.3556701"},"language":"en","primary_location":{"id":"doi:10.1145/3556677.3556701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3556677.3556701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","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/A5100403556","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0003-2176-3835"},"institutions":[{"id":"https://openalex.org/I4210090357","display_name":"Nanjing Forest Police College","ror":"https://ror.org/00adax290","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210090357"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["Nanjing Forest Police College, China"],"affiliations":[{"raw_affiliation_string":"Nanjing Forest Police College, China","institution_ids":["https://openalex.org/I4210090357"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100403556"],"corresponding_institution_ids":["https://openalex.org/I4210090357"],"apc_list":null,"apc_paid":null,"fwci":0.2324,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60105519,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"84","last_page":"88"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14139","display_name":"E-commerce and Technology Innovations","score":0.9330000281333923,"subfield":{"id":"https://openalex.org/subfields/1403","display_name":"Business and International Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T14139","display_name":"E-commerce and Technology Innovations","score":0.9330000281333923,"subfield":{"id":"https://openalex.org/subfields/1403","display_name":"Business and International Management"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8434246778488159},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.8152744174003601},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.7692742943763733},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7536933422088623},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5548282861709595},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.48587316274642944},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4804670214653015},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.46742743253707886},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.43417099118232727},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4283676743507385},{"id":"https://openalex.org/keywords/zoom","display_name":"Zoom","score":0.42087456583976746},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41850847005844116},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4017288386821747}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8434246778488159},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.8152744174003601},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.7692742943763733},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7536933422088623},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5548282861709595},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.48587316274642944},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4804670214653015},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.46742743253707886},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.43417099118232727},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4283676743507385},{"id":"https://openalex.org/C124913957","wikidata":"https://www.wikidata.org/wiki/Q1232548","display_name":"Zoom","level":3,"score":0.42087456583976746},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41850847005844116},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4017288386821747},{"id":"https://openalex.org/C15336307","wikidata":"https://www.wikidata.org/wiki/Q1766051","display_name":"Lens (geology)","level":2,"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/C78762247","wikidata":"https://www.wikidata.org/wiki/Q1273174","display_name":"Petroleum engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3556677.3556701","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3556677.3556701","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2022 6th International Conference on Deep Learning Technologies","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.44999998807907104,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2618206831","https://openalex.org/W2747646026","https://openalex.org/W2802528196","https://openalex.org/W2907076165"],"related_works":["https://openalex.org/W2788595494","https://openalex.org/W2244676099","https://openalex.org/W3172556642","https://openalex.org/W3157456843","https://openalex.org/W1990150827","https://openalex.org/W2036676347","https://openalex.org/W1589307369","https://openalex.org/W3165790285","https://openalex.org/W2971551846","https://openalex.org/W2965594636"],"abstract_inverted_index":{"In":[0],"this":[1,168],"paper,":[2],"a":[3,126,157],"video":[4,71,149,177,181,205],"moving":[5,184,201],"object":[6,23,39,45,49,76,97,111,119,123,140,163,185],"tracking":[7,14,22,44,90,96,186],"method":[8,197],"based":[9],"on":[10],"SVM":[11,59,159],"and":[12,40,50,62,78,113,129,153,164,207],"Meanshift":[13,89],"algorithm":[15,91],"is":[16,24,46,65,188],"proposed.":[17],"The":[18,190],"location":[19,77],"of":[20,30,37,109,139,147,175],"the":[21,27,31,34,38,43,48,58,63,69,75,79,84,88,94,101,106,110,118,122,131,137,144,148,162,172,176,179,195,200,204],"selected":[25],"in":[26,203],"initial":[28],"image":[29,72,81,183],"sports":[32],"video,":[33],"feature":[35,52],"vectors":[36,53],"background":[41,51,80,114,165],"around":[42],"obtained,":[47],"are":[54],"used":[55,66],"to":[56,67,73,82,92,116,134,136,170],"train":[57,156],"binary":[60],"classifier,":[61],"classifier":[64,160],"classify":[68],"next":[70,173],"track":[74,171,199],"obtain":[83],"confidence":[85,102],"map.":[86],"Use":[87],"get":[93],"current":[95],"center":[98,107],"position":[99,108],"within":[100],"map":[103],"range,":[104],"move":[105],"frame":[112,115,124,146,174],"reach":[117],"position,":[120],"zoom":[121],"at":[125,167],"10%":[127],"scale,":[128],"select":[130],"best":[132],"one":[133],"adapt":[135],"change":[138],"size.":[141],"Determines":[142],"if":[143,154],"last":[145],"has":[150],"been":[151],"tracked,":[152],"not,":[155],"new":[158],"using":[161],"pixels":[166],"time":[169],"until":[178],"entire":[180],"sequence":[182],"task":[187],"completed.":[189],"experimental":[191],"results":[192],"show":[193],"that":[194],"proposed":[196],"can":[198],"objects":[202],"real-time":[206],"accurately.":[208]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}