{"id":"https://openalex.org/W583878786","doi":null,"title":"Pedestrian Detection using a boosted cascade of Histogram of Oriented Gradients.","display_name":"Pedestrian Detection using a boosted cascade of Histogram of Oriented Gradients.","publication_year":2014,"publication_date":"2014-09-10","ids":{"openalex":"https://openalex.org/W583878786","mag":"583878786"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.938.7893","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.938.7893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://upcommons.upc.edu/bitstream/handle/2099.1/22837/THESIS_CristinaRuizSancho.pdf%3Bjsessionid%3D5B374458BA64DD120CD56D1BD0BE31EC?sequence%3D4","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5038659372","display_name":"Cristina Ruiz Sancho","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cristina Ruiz Sancho","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5038659372"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2567,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.60236478,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.47999998927116394,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.47999998927116394,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.3610999882221222,"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":0.0357000008225441,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"pedestrian detection","score":1.0},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"histogram of oriented gradients","score":0.9520000219345093},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.7229999899864197},{"id":"https://openalex.org/keywords/feature-selection","display_name":"feature selection","score":0.9409999847412109},{"id":"https://openalex.org/keywords/training-sample-size","display_name":"training sample size","score":0.7879999876022339}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.9390202760696411},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6972611546516418},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6757432818412781},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6193143129348755},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.6128612160682678},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5988019108772278},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5864275693893433},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5124824047088623},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.5013344287872314},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45744559168815613},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4513283967971802},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4478698968887329},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.42103028297424316},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.41283249855041504},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3160463571548462},{"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/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"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":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.938.7893","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.938.7893","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://upcommons.upc.edu/bitstream/handle/2099.1/22837/THESIS_CristinaRuizSancho.pdf%3Bjsessionid%3D5B374458BA64DD120CD56D1BD0BE31EC?sequence%3D4","raw_type":"text"},{"id":"mag:583878786","doi":null,"is_oa":false,"landing_page_url":"https://upcommons.upc.edu/bitstream/2099.1/22837/4/THESIS_CristinaRuizSancho.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5142999887466431}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W137193333","https://openalex.org/W396735907","https://openalex.org/W1439654991","https://openalex.org/W1551519658","https://openalex.org/W1565377632","https://openalex.org/W1594957066","https://openalex.org/W1595717062","https://openalex.org/W1608462934","https://openalex.org/W1906375339","https://openalex.org/W1917380066","https://openalex.org/W1932758808","https://openalex.org/W1960210438","https://openalex.org/W1987118352","https://openalex.org/W1988790447","https://openalex.org/W1992825118","https://openalex.org/W2005489441","https://openalex.org/W2028125713","https://openalex.org/W2031454541","https://openalex.org/W2044465660","https://openalex.org/W2057175746","https://openalex.org/W2074584355","https://openalex.org/W2089181482","https://openalex.org/W2096112371","https://openalex.org/W2096352448","https://openalex.org/W2099355420","https://openalex.org/W2099816072","https://openalex.org/W2101531944","https://openalex.org/W2107634464","https://openalex.org/W2107925163","https://openalex.org/W2109325523","https://openalex.org/W2110482401","https://openalex.org/W2120240539","https://openalex.org/W2120907774","https://openalex.org/W2121899951","https://openalex.org/W2131344117","https://openalex.org/W2132964930","https://openalex.org/W2135125546","https://openalex.org/W2142645932","https://openalex.org/W2144679084","https://openalex.org/W2151103935","https://openalex.org/W2152473410","https://openalex.org/W2154683974","https://openalex.org/W2156539399","https://openalex.org/W2156909104","https://openalex.org/W2161969291","https://openalex.org/W2162359196","https://openalex.org/W2164598857","https://openalex.org/W2165984708","https://openalex.org/W2168020168","https://openalex.org/W2169671170","https://openalex.org/W2187098286","https://openalex.org/W2313843474","https://openalex.org/W2548197316","https://openalex.org/W2911964244","https://openalex.org/W2912934387","https://openalex.org/W2914885528","https://openalex.org/W3085162807"],"related_works":["https://openalex.org/W7113315752","https://openalex.org/W2362984806","https://openalex.org/W4229875865","https://openalex.org/W2908697126","https://openalex.org/W328377050","https://openalex.org/W1966500826","https://openalex.org/W315752581","https://openalex.org/W2077014319","https://openalex.org/W1967873456","https://openalex.org/W3167799673"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,56,77],"has":[2],"been":[3,73],"an":[4],"active":[5],"area":[6],"of":[7,22,24,47,60,83,88,100,108,120,122,145],"research":[8],"in":[9,65,86],"recent":[10],"years;":[11],"its":[12],"interest":[13],"relies":[14],"on":[15,20,105],"the":[16,25,61,98,106,143,148,155],"potential":[17],"positive":[18],"impact":[19],"quality":[21],"life":[23],"related":[26],"applications":[27],"(surveillance":[28],"systems,":[29],"automotive":[30],"safety,":[31],"robotics,":[32],"multimedia":[33],"content":[34],"analysis,":[35],"assistive":[36],"technology":[37],"and":[38,51,90],"advanced":[39],"interactive":[40],"interfaces,":[41],"among":[42],"others).":[43],"The":[44,92],"large":[45],"variability":[46],"human":[48],"appearances,":[49],"poses":[50],"context":[52],"conditions":[53],"makes":[54],"pedestrian":[55,76,110,114],"to":[57,96],"be":[58],"one":[59],"most":[62],"challenging":[63],"tasks":[64],"computer":[66],"vision.":[67],"Although":[68],"many":[69],"significant":[70,139],"approaches":[71],"have":[72],"proposed":[74],"lately,":[75],"still":[78],"offers":[79],"a":[80,109,113,117,130],"wide":[81],"framework":[82],"improvement,":[84],"mainly":[85],"terms":[87],"accuracy":[89],"efficiency.":[91],"present":[93],"thesis":[94],"aims":[95],"study":[97],"influence":[99],"different":[101],"training":[102,146],"parameters":[103],"values":[104],"performance":[107],"detector.":[111],"First,":[112],"detector,":[115],"using":[116],"boosted":[118],"cascade":[119],"Histograms":[121],"Oriented":[123],"Gradients,":[124],"is":[125,133],"built":[126],"from":[127],"scratch.":[128],"Afterwards,":[129],"sensitivity":[131],"analysis":[132],"carried":[134],"out":[135],"taking":[136],"into":[137],"account":[138],"variables,":[140],"such":[141],"as":[142],"number":[144],"samples,":[147],"feature":[149,156],"(HOG)":[150],"or":[151],"classifier":[152],"(SVM)":[153],"parameters,":[154],"selection":[157],"technique,":[158]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2016-06-24T00:00:00"}