{"id":"https://openalex.org/W2018041122","doi":"https://doi.org/10.1109/iccvw.2009.5457471","title":"A particle swarm optimization approach for multi-objects tracking in crowded scene","display_name":"A particle swarm optimization approach for multi-objects tracking in crowded scene","publication_year":2009,"publication_date":"2009-09-01","ids":{"openalex":"https://openalex.org/W2018041122","doi":"https://doi.org/10.1109/iccvw.2009.5457471","mag":"2018041122"},"language":"en","primary_location":{"id":"doi:10.1109/iccvw.2009.5457471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2009.5457471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops","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/A5000328017","display_name":"Myo Thida","orcid":"https://orcid.org/0000-0002-5259-4010"},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Myo Thida","raw_affiliation_strings":["Institute for Infocomm Research, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000187752","display_name":"Paolo Remagnino","orcid":"https://orcid.org/0000-0002-9168-7746"},"institutions":[{"id":"https://openalex.org/I205051169","display_name":"Kingston University","ror":"https://ror.org/05bbqza97","country_code":"GB","type":"education","lineage":["https://openalex.org/I205051169"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Paolo Remagnino","raw_affiliation_strings":["Digital Imaging Research Center, Kingston University, London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Digital Imaging Research Center, Kingston University, London, UK","institution_ids":["https://openalex.org/I205051169"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079819926","display_name":"How\u2010Lung Eng","orcid":null},"institutions":[{"id":"https://openalex.org/I3005327000","display_name":"Institute for Infocomm Research","ror":"https://ror.org/053rfa017","country_code":"SG","type":"facility","lineage":["https://openalex.org/I115228651","https://openalex.org/I3005327000","https://openalex.org/I91275662"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"How-Lung Eng","raw_affiliation_strings":["Institute for Infocomm Research, Singapore"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Infocomm Research, Singapore","institution_ids":["https://openalex.org/I3005327000"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9855,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.77208469,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1209","last_page":"1215"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9911999702453613,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9717000126838684,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particle-swarm-optimization","display_name":"Particle swarm optimization","score":0.7206073999404907},{"id":"https://openalex.org/keywords/scale-invariant-feature-transform","display_name":"Scale-invariant feature transform","score":0.6899149417877197},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6723697185516357},{"id":"https://openalex.org/keywords/local-optimum","display_name":"Local optimum","score":0.560634434223175},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5580360293388367},{"id":"https://openalex.org/keywords/multi-swarm-optimization","display_name":"Multi-swarm optimization","score":0.5368623733520508},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.5297083854675293},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5291205644607544},{"id":"https://openalex.org/keywords/cma-es","display_name":"CMA-ES","score":0.5250768065452576},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.516887903213501},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.48640966415405273},{"id":"https://openalex.org/keywords/fitness-function","display_name":"Fitness function","score":0.484836220741272},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4813016951084137},{"id":"https://openalex.org/keywords/swarm-behaviour","display_name":"Swarm behaviour","score":0.46256670355796814},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.35645759105682373},{"id":"https://openalex.org/keywords/evolution-strategy","display_name":"Evolution strategy","score":0.30962884426116943},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.22724637389183044},{"id":"https://openalex.org/keywords/evolutionary-computation","display_name":"Evolutionary computation","score":0.2013438642024994},{"id":"https://openalex.org/keywords/genetic-algorithm","display_name":"Genetic algorithm","score":0.1499331295490265},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09554579854011536}],"concepts":[{"id":"https://openalex.org/C85617194","wikidata":"https://www.wikidata.org/wiki/Q2072794","display_name":"Particle swarm optimization","level":2,"score":0.7206073999404907},{"id":"https://openalex.org/C61265191","wikidata":"https://www.wikidata.org/wiki/Q767770","display_name":"Scale-invariant feature transform","level":3,"score":0.6899149417877197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6723697185516357},{"id":"https://openalex.org/C141934464","wikidata":"https://www.wikidata.org/wiki/Q3305386","display_name":"Local optimum","level":2,"score":0.560634434223175},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5580360293388367},{"id":"https://openalex.org/C122357587","wikidata":"https://www.wikidata.org/wiki/Q6934508","display_name":"Multi-swarm optimization","level":3,"score":0.5368623733520508},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.5297083854675293},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5291205644607544},{"id":"https://openalex.org/C205555498","wikidata":"https://www.wikidata.org/wiki/Q505588","display_name":"CMA-ES","level":4,"score":0.5250768065452576},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.516887903213501},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48640966415405273},{"id":"https://openalex.org/C176066374","wikidata":"https://www.wikidata.org/wiki/Q629118","display_name":"Fitness function","level":3,"score":0.484836220741272},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4813016951084137},{"id":"https://openalex.org/C181335050","wikidata":"https://www.wikidata.org/wiki/Q14915018","display_name":"Swarm behaviour","level":2,"score":0.46256670355796814},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.35645759105682373},{"id":"https://openalex.org/C207002847","wikidata":"https://www.wikidata.org/wiki/Q2912857","display_name":"Evolution strategy","level":3,"score":0.30962884426116943},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.22724637389183044},{"id":"https://openalex.org/C105902424","wikidata":"https://www.wikidata.org/wiki/Q1197129","display_name":"Evolutionary computation","level":2,"score":0.2013438642024994},{"id":"https://openalex.org/C8880873","wikidata":"https://www.wikidata.org/wiki/Q187787","display_name":"Genetic algorithm","level":2,"score":0.1499331295490265},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09554579854011536},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccvw.2009.5457471","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccvw.2009.5457471","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W148413188","https://openalex.org/W1518507396","https://openalex.org/W1995903777","https://openalex.org/W2023789326","https://openalex.org/W2038735287","https://openalex.org/W2042175659","https://openalex.org/W2057513549","https://openalex.org/W2109364787","https://openalex.org/W2124386111","https://openalex.org/W2125907508","https://openalex.org/W2132103241","https://openalex.org/W2159128898","https://openalex.org/W2160337655","https://openalex.org/W2161406034","https://openalex.org/W2165797462","https://openalex.org/W2170858806","https://openalex.org/W2543580944","https://openalex.org/W6685004768"],"related_works":["https://openalex.org/W1492325323","https://openalex.org/W1579744901","https://openalex.org/W2510724351","https://openalex.org/W3087341323","https://openalex.org/W2465356436","https://openalex.org/W2552260697","https://openalex.org/W2078076985","https://openalex.org/W2592891920","https://openalex.org/W2734610626","https://openalex.org/W2559740600"],"abstract_inverted_index":{"This":[0,121],"paper":[1],"presents":[2],"a":[3,50,60,73,78,112],"new":[4],"particle":[5,51],"swarm":[6,52],"optimization-based":[7],"algorithm":[8],"for":[9,36],"tracking":[10,84,123],"objects":[11,124],"in":[12,43],"crowded":[13,119],"scenes.":[14,120],"The":[15,56],"proposed":[16,107],"method":[17,108],"exploits":[18],"the":[19,31,37,41,44,68,99],"properties":[20],"of":[21,40,83,114,118],"local":[22,90],"feature":[23,91,94,102],"descriptors":[24,95],"and":[25,130],"color-based":[26,79],"covariance":[27,80],"matrix":[28],"to":[29],"model":[30],"targets.":[32],"Then,":[33],"optimal":[34],"search":[35],"best":[38],"match":[39],"targets":[42],"successive":[45],"frames":[46],"is":[47,59,86],"performed":[48],"using":[49,77,98],"optimization":[53],"(PSO)":[54],"algorithm.":[55],"PSO,":[57],"which":[58],"population-based":[61],"searching":[62],"algorithm,":[63],"attracts":[64],"all":[65],"particles":[66],"towards":[67],"global":[69],"optima":[70],"based":[71,88],"on":[72,89],"fitness":[74],"function":[75],"defined":[76],"matrix.":[81],"Adaptation":[82],"windows":[85],"obtained":[87],"descriptors.":[92],"Local":[93],"are":[96],"extracted":[97],"scale":[100],"invariant":[101],"transform":[103],"(SIFT)":[104],"method.":[105],"Our":[106],"can":[109],"cope":[110],"with":[111],"number":[113],"challenging":[115],"scenarios":[116],"typical":[117],"includes":[122],"under":[125],"heavy":[126],"occlusions,":[127],"erratic":[128],"motion":[129],"illumination":[131],"changes.":[132]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}