{"id":"https://openalex.org/W2910516032","doi":"https://doi.org/10.3233/jifs-172257","title":"An efficient algorithm for tracking and counting pedestrians based on feature points in video surveillance applications","display_name":"An efficient algorithm for tracking and counting pedestrians based on feature points in video surveillance applications","publication_year":2019,"publication_date":"2019-01-09","ids":{"openalex":"https://openalex.org/W2910516032","doi":"https://doi.org/10.3233/jifs-172257","mag":"2910516032"},"language":"en","primary_location":{"id":"doi:10.3233/jifs-172257","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-172257","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-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/A5022648835","display_name":"S Kanagamalliga","orcid":"https://orcid.org/0000-0001-8206-0610"},"institutions":[{"id":"https://openalex.org/I4210104323","display_name":"Velammal Medical College Hospital and Research Institute","ror":"https://ror.org/00x9zs206","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I4210104323"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"S. Kanagamalliga","raw_affiliation_strings":["Department of ECE, Velammal College of Engineering and Technology, Viraganoor, Madurai, India"],"affiliations":[{"raw_affiliation_string":"Department of ECE, Velammal College of Engineering and Technology, Viraganoor, Madurai, India","institution_ids":["https://openalex.org/I4210104323"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020393171","display_name":"S. Vasuki","orcid":null},"institutions":[{"id":"https://openalex.org/I4210104323","display_name":"Velammal Medical College Hospital and Research Institute","ror":"https://ror.org/00x9zs206","country_code":"IN","type":"healthcare","lineage":["https://openalex.org/I4210104323"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"S. Vasuki","raw_affiliation_strings":["Department of ECE, Velammal College of Engineering and Technology, Viraganoor, Madurai, India"],"affiliations":[{"raw_affiliation_string":"Department of ECE, Velammal College of Engineering and Technology, Viraganoor, Madurai, India","institution_ids":["https://openalex.org/I4210104323"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5022648835"],"corresponding_institution_ids":["https://openalex.org/I4210104323"],"apc_list":null,"apc_paid":null,"fwci":0.5061,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.67639965,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"36","issue":"1","first_page":"67","last_page":"78"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9900000095367432,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.7910058498382568},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7879192233085632},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.6734911799430847},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.635992169380188},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6206656098365784},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5689666867256165},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5579715967178345},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5370798110961914},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5274156332015991},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5078619122505188},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4876185953617096},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.4811635911464691},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.46875834465026855},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4617934226989746},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.461195707321167},{"id":"https://openalex.org/keywords/mean-shift","display_name":"Mean-shift","score":0.43583494424819946},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.40209218859672546},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.2449844479560852},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17896965146064758}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7910058498382568},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7879192233085632},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.6734911799430847},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.635992169380188},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6206656098365784},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5689666867256165},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5579715967178345},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5370798110961914},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5274156332015991},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5078619122505188},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4876185953617096},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.4811635911464691},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.46875834465026855},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4617934226989746},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.461195707321167},{"id":"https://openalex.org/C48548287","wikidata":"https://www.wikidata.org/wiki/Q6803557","display_name":"Mean-shift","level":3,"score":0.43583494424819946},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.40209218859672546},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.2449844479560852},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17896965146064758},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/jifs-172257","is_oa":false,"landing_page_url":"https://doi.org/10.3233/jifs-172257","pdf_url":null,"source":{"id":"https://openalex.org/S179157397","display_name":"Journal of Intelligent & Fuzzy Systems","issn_l":"1064-1246","issn":["1064-1246","1875-8967"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Intelligent & Fuzzy Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W869147768","https://openalex.org/W1126395823","https://openalex.org/W1485542220","https://openalex.org/W1578280683","https://openalex.org/W1965484410","https://openalex.org/W1981690742","https://openalex.org/W2024307315","https://openalex.org/W2039412848","https://openalex.org/W2060022598","https://openalex.org/W2166016416","https://openalex.org/W2185058606","https://openalex.org/W2238054732","https://openalex.org/W2255189111","https://openalex.org/W2336192442","https://openalex.org/W2531115762","https://openalex.org/W2531812527","https://openalex.org/W2562066081","https://openalex.org/W2589765509","https://openalex.org/W2741588248","https://openalex.org/W2769734653"],"related_works":["https://openalex.org/W1481296176","https://openalex.org/W1487824918","https://openalex.org/W2153481672","https://openalex.org/W2386156666","https://openalex.org/W2349207614","https://openalex.org/W2094810659","https://openalex.org/W2335246570","https://openalex.org/W1980446522","https://openalex.org/W2492453441","https://openalex.org/W2971088694"],"abstract_inverted_index":{"Object":[0],"tracking":[1,25,168],"is":[2,42,92,172],"an":[3,80],"efficient":[4,24,173],"technique":[5],"adopted":[6],"in":[7,16,31,94],"video":[8,32],"surveillance":[9],"applications":[10],"for":[11,29],"monitoring":[12],"a":[13,17,22,37,142],"particular":[14],"object":[15,35,61,153],"zone.":[18],"This":[19,64],"paper":[20],"presents":[21],"novel":[23],"and":[26,101,135,157,169],"counting":[27,117,170],"approach":[28],"Pedestrians":[30],"sequences.":[33],"For":[34,76,116],"detection,":[36,154],"Gaussian":[38],"Mixture":[39],"Model":[40],"(GMM)":[41],"used":[43],"to":[44],"obtain":[45],"binary":[46],"masks.":[47],"In":[48],"the":[49,57,60,67,70,78,96,102,118,122,130,136,152,165,175],"Speeded":[50,71,131],"Up":[51,72,132],"Robust":[52,73,133],"Feature":[53,74],"feature":[54,155],"recognition,":[55],"only":[56],"features":[58,98,104,108],"of":[59,69,88,120],"are":[62,99,105,109,126,139],"retained.":[63],"significantly":[65],"improves":[66],"precision":[68],"method.":[75,115],"clustering":[77],"features,":[79],"enhanced":[81],"grouping":[82],"algorithm":[83],"Density":[84],"based":[85,111,128],"Spatial":[86],"Clustering":[87],"Application":[89],"with":[90,141],"Noise":[91],"proposed":[93,149,166],"which":[95],"motion":[97],"grouped":[100],"remaining":[103],"excluded.":[106],"The":[107,148,160],"tracked":[110],"on":[112,129],"optical":[113],"flow":[114],"number":[119],"objects,":[121],"Pedestrian":[123],"eigen":[124,137],"vectors":[125,138],"created":[127],"Features":[134],"trained":[140],"SVM":[143],"(support":[144],"vector":[145],"regression":[146],"machine).":[147],"work":[150],"combines":[151],"extraction,":[156],"objects":[158],"counting.":[159],"experimental":[161],"results":[162],"validate":[163],"that":[164],"pedestrian":[167],"method":[171],"than":[174],"existing":[176],"approaches.":[177]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}