{"id":"https://openalex.org/W2120547665","doi":"https://doi.org/10.1109/ijcnn.2008.4633872","title":"A biologically inspired visual pedestrian detection system","display_name":"A biologically inspired visual pedestrian detection system","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2120547665","doi":"https://doi.org/10.1109/ijcnn.2008.4633872","mag":"2120547665"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2008.4633872","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088693326","display_name":"Fok Hing Chi Tivive","orcid":"https://orcid.org/0000-0001-8961-2289"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":true,"raw_author_name":"Fok Hing Chi Tivive","raw_affiliation_strings":["School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia","institution_ids":["https://openalex.org/I204824540"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072662577","display_name":"Abdesselam Bouzerdoum","orcid":"https://orcid.org/0000-0002-9163-0045"},"institutions":[{"id":"https://openalex.org/I204824540","display_name":"University of Wollongong","ror":"https://ror.org/00jtmb277","country_code":"AU","type":"education","lineage":["https://openalex.org/I204824540"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Abdesselam Bouzerdoum","raw_affiliation_strings":["School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia","Sch. of Electr. Comput. & Telecommun. Eng., Univ. of Wollongong, Wollongong, NSW"],"affiliations":[{"raw_affiliation_string":"School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW, Australia","institution_ids":["https://openalex.org/I204824540"]},{"raw_affiliation_string":"Sch. of Electr. Comput. & Telecommun. Eng., Univ. of Wollongong, Wollongong, NSW","institution_ids":["https://openalex.org/I204824540"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5088693326"],"corresponding_institution_ids":["https://openalex.org/I204824540"],"apc_list":null,"apc_paid":null,"fwci":0.7094,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74686287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"28","issue":null,"first_page":"703","last_page":"709"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9988999962806702,"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":0.9988999962806702,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9944000244140625,"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"}},{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9879999756813049,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.7969915866851807},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7786493301391602},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7355554103851318},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6296818852424622},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5725836753845215},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5511981844902039},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5194796323776245},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5037783980369568},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.4713843762874603},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4650503396987915},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4316118061542511},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.42609700560569763},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.30040475726127625},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1265973448753357}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7969915866851807},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7786493301391602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7355554103851318},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6296818852424622},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5725836753845215},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5511981844902039},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5194796323776245},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5037783980369568},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.4713843762874603},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4650503396987915},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4316118061542511},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.42609700560569763},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.30040475726127625},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1265973448753357},{"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn.2008.4633872","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633872","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"},{"id":"pmh:oai:ro.uow.edu.au:infopapers-1695","is_oa":false,"landing_page_url":"https://ro.uow.edu.au/infopapers/690","pdf_url":null,"source":{"id":"https://openalex.org/S4306400510","display_name":"Research Online (University of Wollongong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I204824540","host_organization_name":"University of Wollongong","host_organization_lineage":["https://openalex.org/I204824540"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Faculty of Informatics - Papers (Archive)","raw_type":"presentation"},{"id":"pmh:oai:figshare.com:article/27722616","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"}],"best_oa_location":{"id":"pmh:oai:figshare.com:article/27722616","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196282","display_name":"Figshare","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210132348","host_organization_name":"Figshare (United Kingdom)","host_organization_lineage":["https://openalex.org/I4210132348"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6700000166893005}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1594551768","https://openalex.org/W1605874634","https://openalex.org/W1992825118","https://openalex.org/W1996535941","https://openalex.org/W2010197352","https://openalex.org/W2040013760","https://openalex.org/W2089181482","https://openalex.org/W2095809036","https://openalex.org/W2107457018","https://openalex.org/W2112796928","https://openalex.org/W2112857506","https://openalex.org/W2117823680","https://openalex.org/W2118605474","https://openalex.org/W2120236285","https://openalex.org/W2120284346","https://openalex.org/W2125264278","https://openalex.org/W2133771133","https://openalex.org/W2134184411","https://openalex.org/W2135457387","https://openalex.org/W2146611287","https://openalex.org/W2149072507","https://openalex.org/W2154740476","https://openalex.org/W2155482699","https://openalex.org/W2161969291","https://openalex.org/W2163272476","https://openalex.org/W3151111735","https://openalex.org/W4230484815","https://openalex.org/W7015289254"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2392615019","https://openalex.org/W2879046444","https://openalex.org/W2791998051","https://openalex.org/W2316461419","https://openalex.org/W2578456052","https://openalex.org/W1795042746","https://openalex.org/W2913302899","https://openalex.org/W2335751644"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"present":[4],"a":[5,19,57,84],"biologically":[6],"inspired":[7],"method":[8,15],"for":[9,64],"detecting":[10],"pedestrians":[11],"in":[12,49],"images.":[13],"The":[14,30,68,87],"is":[16,34,92,104],"based":[17],"on":[18,94],"convolutional":[20],"neural":[21],"network":[22,32],"architecture,":[23],"which":[24,78],"combines":[25],"feature":[26,76],"extraction":[27],"and":[28,37,101,113],"classification.":[29],"proposed":[31,88],"architecture":[33],"much":[35],"simpler":[36],"easier":[38],"to":[39,83,106],"train":[40],"than":[41],"earlier":[42],"versions.":[43],"It":[44],"differs":[45],"from":[46],"its":[47,102],"predecessors":[48],"that":[50],"the":[51,95,107],"first":[52],"processing":[53,70],"layer":[54,71],"consists":[55],"of":[56,59,109],"set":[58],"pre-defined":[60],"nonlinear":[61],"derivative":[62],"filters":[63],"computing":[65],"gradient":[66],"information.":[67],"subsequent":[69],"has":[72],"trainable":[73],"shunting":[74],"inhibitory":[75],"detectors,":[77],"are":[79],"used":[80],"as":[81],"inputs":[82],"pattern":[85],"classifier.":[86],"pedestrian":[89,97],"detection":[90],"system":[91],"evaluated":[93],"DaimlerChrysler":[96],"classification":[98],"benchmark":[99],"database":[100],"performance":[103,108],"compared":[105],"support":[110],"vector":[111],"machines":[112],"Adaboost":[114],"classifiers.":[115]},"counts_by_year":[{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}