{"id":"https://openalex.org/W2002513839","doi":"https://doi.org/10.1145/2393216.2393303","title":"Real time recognition of pedestrian and vehicles from videos","display_name":"Real time recognition of pedestrian and vehicles from videos","publication_year":2012,"publication_date":"2012-10-26","ids":{"openalex":"https://openalex.org/W2002513839","doi":"https://doi.org/10.1145/2393216.2393303","mag":"2002513839"},"language":"en","primary_location":{"id":"doi:10.1145/2393216.2393303","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2393216.2393303","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology","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/A5005527689","display_name":"S. Gokul","orcid":"https://orcid.org/0000-0002-6757-6691"},"institutions":[{"id":"https://openalex.org/I20497027","display_name":"Cochin University of Science and Technology","ror":"https://ror.org/00a4kqq17","country_code":"IN","type":"education","lineage":["https://openalex.org/I20497027"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"S. Gokul","raw_affiliation_strings":["Cochin University of Science and Technology, Cochin, India"],"affiliations":[{"raw_affiliation_string":"Cochin University of Science and Technology, Cochin, India","institution_ids":["https://openalex.org/I20497027"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011155015","display_name":"G. Santhosh Kumar","orcid":null},"institutions":[{"id":"https://openalex.org/I20497027","display_name":"Cochin University of Science and Technology","ror":"https://ror.org/00a4kqq17","country_code":"IN","type":"education","lineage":["https://openalex.org/I20497027"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"G. Santhosh Kumar","raw_affiliation_strings":["Cochin University of Science and Technology, Cochin, India"],"affiliations":[{"raw_affiliation_string":"Cochin University of Science and Technology, Cochin, India","institution_ids":["https://openalex.org/I20497027"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034441570","display_name":"M. Sreeraj","orcid":null},"institutions":[{"id":"https://openalex.org/I20497027","display_name":"Cochin University of Science and Technology","ror":"https://ror.org/00a4kqq17","country_code":"IN","type":"education","lineage":["https://openalex.org/I20497027"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"M. Sreeraj","raw_affiliation_strings":["Cochin University of Science and Technology, Cochin, India"],"affiliations":[{"raw_affiliation_string":"Cochin University of Science and Technology, Cochin, India","institution_ids":["https://openalex.org/I20497027"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005527689"],"corresponding_institution_ids":["https://openalex.org/I20497027"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.08028132,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"517","last_page":"523"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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":0.9991999864578247,"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":0.996999979019165,"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/background-subtraction","display_name":"Background subtraction","score":0.7841825485229492},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7566728591918945},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7490585446357727},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6777823567390442},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.611260712146759},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5372886657714844},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5048945546150208},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.48426884412765503},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.47524237632751465},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47090840339660645},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.44742822647094727},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43024879693984985},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.19046375155448914},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.12091761827468872}],"concepts":[{"id":"https://openalex.org/C32653426","wikidata":"https://www.wikidata.org/wiki/Q3813641","display_name":"Background subtraction","level":3,"score":0.7841825485229492},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7566728591918945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7490585446357727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6777823567390442},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.611260712146759},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5372886657714844},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5048945546150208},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.48426884412765503},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.47524237632751465},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47090840339660645},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.44742822647094727},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43024879693984985},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.19046375155448914},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.12091761827468872},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2393216.2393303","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2393216.2393303","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W20773523","https://openalex.org/W1584413730","https://openalex.org/W1690959182","https://openalex.org/W1967107373","https://openalex.org/W1993488146","https://openalex.org/W2050750818","https://openalex.org/W2056415478","https://openalex.org/W2063828651","https://openalex.org/W2069514083","https://openalex.org/W2093537603","https://openalex.org/W2114669273","https://openalex.org/W2119605622","https://openalex.org/W2120930188","https://openalex.org/W2145023731","https://openalex.org/W2156029399"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2538580916","https://openalex.org/W2981141433","https://openalex.org/W2361251692","https://openalex.org/W1987287817","https://openalex.org/W2802018156","https://openalex.org/W2123129869","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W3094443322"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,13,103,127],"new":[4],"approach":[5],"in":[6,36],"recognition":[7,25,129],"of":[8,21,26,50,56,75,112,131,155],"moving":[9,57,77,96],"objects":[10,58,97],"captured":[11,38],"by":[12],"surveillance":[14,40],"camera.":[15],"We":[16,107],"have":[17,108],"limited":[18],"our":[19],"area":[20],"study":[22],"to":[23,85,92,102,151],"the":[24,37,51,54,67,76,83,95,110,113,153,156],"pedestrians":[27,104,134,170],"and":[28,62,135,147,159,162,171],"vehicles":[29],"as":[30,41,43,118,120,142],"it":[31,101],"has":[32,166],"ever":[33],"increasing":[34],"importance":[35],"security":[39],"well":[42,119],"traffic":[44],"monitoring":[45],"systems.":[46],"The":[47,124],"primary":[48],"phase":[49],"method":[52],"is":[53,79],"detection":[55],"using":[59],"background":[60],"subtraction":[61],"edge":[63],"based":[64],"subtraction.":[65],"In":[66],"next":[68],"phase,":[69],"Speeded":[70],"Up":[71],"Robust":[72],"Feature":[73],"(SURF)":[74],"object":[78],"extracted":[80],"along":[81],"with":[82,115],"height":[84],"width":[86],"ratio.":[87],"These":[88],"features":[89],"are":[90,149],"used":[91,150],"correctly":[93],"recognize":[94],"which":[98],"then":[99],"differentiate":[100],"or":[105],"vehicles.":[106,138,172],"tested":[109],"performance":[111,154],"system":[114,125,158],"sample":[116],"videos":[117],"real":[121],"time":[122],"videos.":[123],"shows":[126],"considerable":[128],"rate":[130],"70%":[132],"for":[133,137,164,169],"80%":[136],"Statistical":[139],"measures":[140],"such":[141],"false":[143],"discovery":[144],"rate,":[145],"recall":[146,161],"precision":[148,165],"measure":[152],"proposed":[157],"0.75%":[160],"0.97%":[163],"been":[167],"obtained":[168]},"counts_by_year":[{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}