{"id":"https://openalex.org/W1939626751","doi":"https://doi.org/10.5220/0004677003540361","title":"Environment Adaptive Pedestrian Detection using In-vehicle Camera and GPS","display_name":"Environment Adaptive Pedestrian Detection using In-vehicle Camera and GPS","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W1939626751","doi":"https://doi.org/10.5220/0004677003540361","mag":"1939626751"},"language":"en","primary_location":{"id":"doi:10.5220/0004677003540361","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0004677003540361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Computer Vision Theory and Applications","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":[],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.9755,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.78478664,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"354","last_page":"361"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994999766349792,"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":0.9994999766349792,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9915000200271606,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9908000230789185,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/pedestrian","display_name":"Pedestrian","score":0.7943947315216064},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7873133420944214},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.751885175704956},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7477999925613403},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6769751310348511},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5987688899040222},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.5876209735870361},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10586240887641907}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7943947315216064},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7873133420944214},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.751885175704956},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7477999925613403},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6769751310348511},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5987688899040222},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.5876209735870361},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10586240887641907},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5220/0004677003540361","is_oa":false,"landing_page_url":"https://doi.org/10.5220/0004677003540361","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 9th International Conference on Computer Vision Theory and Applications","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1625255723","https://openalex.org/W1677409904","https://openalex.org/W2013039598","https://openalex.org/W2081922427","https://openalex.org/W2086736092","https://openalex.org/W2118585731","https://openalex.org/W2121496627","https://openalex.org/W2152369758","https://openalex.org/W2161969291","https://openalex.org/W2164202775","https://openalex.org/W2296428857","https://openalex.org/W2886159057"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2101960027","https://openalex.org/W2197846993","https://openalex.org/W1976827262","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W3122828758","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"accurate":[3,96],"pedestrian":[4,97],"detection":[5,53,98],"from":[6],"in-vehicle":[7],"camera":[8],"images":[9,50,81,123],"is":[10,32,67,91],"focused":[11],"to":[12,34,69,83,93,105,151],"develop":[13],"a":[14,36,134],"safety":[15],"driving":[16,58,88,118],"assistance":[17],"system.":[18],"Currently,":[19],"successful":[20],"methods":[21],"are":[22],"based":[23],"on":[24],"statistical":[25],"learning.":[26],"However,":[27],"in":[28,45,57],"such":[29],"methods,":[30],"it":[31,66],"necessary":[33],"prepare":[35],"large":[37],"amount":[38],"of":[39,48,113,117,121,126,147],"training":[40,49,64,80,125],"images.":[41],"Thus,":[42],"the":[43,46,52,106,145,148],"decrease":[44],"number":[47],"degrades":[51],"accuracy.":[54],"That":[55],"is,":[56],"environments":[59],"with":[60,137],"few":[61],"or":[62],"no":[63],"images,":[65],"difficult":[68],"detect":[70],"pedestrians":[71],"accurately.":[72],"Therefore,":[73],"we":[74,143],"propose":[75],"an":[76,101],"approach":[77],"that":[78],"collects":[79],"automatically":[82],"build":[84],"classifiers":[85,127],"for":[86,128],"various":[87],"environments.":[89],"This":[90],"expected":[92],"realize":[94],"highly":[95],"by":[99],"using":[100],"appropriate":[102],"classifier":[103,136],"corresponding":[104],"current":[107],"location.":[108],"The":[109],"proposed":[110],"method":[111,149],"consists":[112],"three":[114],"steps;":[115],"Classification":[116],"scenes,":[119],"collection":[120],"non-pedestrian":[122],"and":[124,132],"each":[129],"scene":[130],"class,":[131],"associating":[133],"scene-class-specific":[135],"GPS":[138],"location":[139],"information.":[140],"Through":[141],"experiments,":[142],"confirmed":[144],"effectiveness":[146],"compared":[150],"baseline":[152],"methods.":[153]},"counts_by_year":[{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}