{"id":"https://openalex.org/W1973459734","doi":"https://doi.org/10.1145/2638404.2638468","title":"Improving HOG descriptor accuracy using non-linear multi-scale space in people detection","display_name":"Improving HOG descriptor accuracy using non-linear multi-scale space in people detection","publication_year":2014,"publication_date":"2014-03-28","ids":{"openalex":"https://openalex.org/W1973459734","doi":"https://doi.org/10.1145/2638404.2638468","mag":"1973459734"},"language":"en","primary_location":{"id":"doi:10.1145/2638404.2638468","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2638404.2638468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 ACM Southeast Regional Conference","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/A5079184024","display_name":"Zhaohua Yi","orcid":null},"institutions":[{"id":"https://openalex.org/I368840534","display_name":"University of Mississippi","ror":"https://ror.org/02teq1165","country_code":"US","type":"education","lineage":["https://openalex.org/I368840534","https://openalex.org/I4210141039"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Zhaohua Yi","raw_affiliation_strings":["The University Of Mississippi","The University of Mississippi"],"affiliations":[{"raw_affiliation_string":"The University Of Mississippi","institution_ids":["https://openalex.org/I368840534"]},{"raw_affiliation_string":"The University of Mississippi","institution_ids":["https://openalex.org/I368840534"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109997723","display_name":"Jianxia Xue","orcid":null},"institutions":[{"id":"https://openalex.org/I368840534","display_name":"University of Mississippi","ror":"https://ror.org/02teq1165","country_code":"US","type":"education","lineage":["https://openalex.org/I368840534","https://openalex.org/I4210141039"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jianxia Xue","raw_affiliation_strings":["The University Of Mississippi","The University of Mississippi"],"affiliations":[{"raw_affiliation_string":"The University Of Mississippi","institution_ids":["https://openalex.org/I368840534"]},{"raw_affiliation_string":"The University of Mississippi","institution_ids":["https://openalex.org/I368840534"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5079184024"],"corresponding_institution_ids":["https://openalex.org/I368840534"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.05240221,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9983000159263611,"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":0.9980000257492065,"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":0.7417709827423096},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7249031662940979},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7080617547035217},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6850079298019409},{"id":"https://openalex.org/keywords/scale-space","display_name":"Scale space","score":0.6841139793395996},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.641274094581604},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.639138400554657},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5821115374565125},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5807456970214844},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5566638708114624},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5265331864356995},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.5100727677345276},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4594247341156006},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3349760174751282},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.15342870354652405},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.14669954776763916},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.09259900450706482}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7417709827423096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7249031662940979},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7080617547035217},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6850079298019409},{"id":"https://openalex.org/C99102927","wikidata":"https://www.wikidata.org/wiki/Q3058184","display_name":"Scale space","level":4,"score":0.6841139793395996},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.641274094581604},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.639138400554657},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5821115374565125},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5807456970214844},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5566638708114624},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5265331864356995},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.5100727677345276},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4594247341156006},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3349760174751282},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.15342870354652405},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.14669954776763916},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.09259900450706482},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2638404.2638468","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2638404.2638468","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2014 ACM Southeast Regional Conference","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G7314163092","display_name":null,"funder_award_id":"EPS-0903787","funder_id":"https://openalex.org/F4320338283","funder_display_name":"Office of Experimental Program to Stimulate Competitive Research"}],"funders":[{"id":"https://openalex.org/F4320338283","display_name":"Office of Experimental Program to Stimulate Competitive Research","ror":"https://ror.org/04k9mqs78"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1973252784","https://openalex.org/W1975961009","https://openalex.org/W1976333597","https://openalex.org/W1976526581","https://openalex.org/W1982428585","https://openalex.org/W1987716715","https://openalex.org/W2022735534","https://openalex.org/W2031454541","https://openalex.org/W2048710758","https://openalex.org/W2062895895","https://openalex.org/W2073246097","https://openalex.org/W2077513643","https://openalex.org/W2120419212","https://openalex.org/W2150134853","https://openalex.org/W2161969291","https://openalex.org/W2164598857","https://openalex.org/W2548197316","https://openalex.org/W2913429812","https://openalex.org/W6729651492","https://openalex.org/W6803376173"],"related_works":["https://openalex.org/W1977648593","https://openalex.org/W2348780717","https://openalex.org/W2780165746","https://openalex.org/W2602033589","https://openalex.org/W4235475963","https://openalex.org/W4252030899","https://openalex.org/W2071599417","https://openalex.org/W2069019032","https://openalex.org/W2374077716","https://openalex.org/W1993364152"],"abstract_inverted_index":{"People":[0],"detection":[1,43,74,83,87,122,138],"is":[2,66,124],"commonly":[3],"the":[4,58,91,104,111,118],"first":[5],"automation":[6],"step":[7],"in":[8,41,75,126,132],"many":[9],"computer":[10],"vision":[11],"based":[12],"applications":[13],"such":[14],"as":[15],"3D":[16],"body":[17],"pose":[18],"estimation":[19],"and":[20,44,60,103],"reconstruction.":[21],"The":[22],"feature":[23],"extraction":[24],"algorithm":[25],"of":[26,28,50,107,113],"Histogram":[27],"Oriented":[29],"Gradient":[30],"(HOG)":[31],"combined":[32],"with":[33,96],"supporting":[34],"vector":[35],"machine":[36],"(SVM)":[37],"has":[38],"been":[39],"successful":[40],"pedestrian":[42],"had":[45],"since":[46],"inspired":[47],"a":[48],"series":[49],"variational":[51],"algorithms":[52],"that":[53,117,130],"improves":[54],"its":[55],"accuracy.":[56],"However,":[57],"extensions":[59],"enhancements":[61],"to":[62,90,101],"HOG":[63,73],"on":[64],"multi-scale":[65,78,94,121],"rare.":[67],"In":[68],"this":[69],"paper,":[70],"we":[71],"propose":[72],"non-linearly":[76],"filtered":[77],"image":[79],"spaces.":[80],"Our":[81],"people":[82],"experiment":[84],"shows":[85],"significant":[86],"improvement":[88],"comparing":[89],"traditional":[92],"linear":[93],"detections,":[95],"recall":[97,112],"increase":[98,106],"from":[99],"26%":[100],"67%,":[102],"precision":[105],"3.8":[108],"times":[109],"at":[110],"26%.":[114],"We":[115],"conclude":[116],"proposed":[119],"non-linear":[120],"framework":[123],"necessary":[125],"detecting":[127],"those":[128],"objects":[129],"are":[131],"much":[133],"larger":[134],"size":[135],"than":[136],"trained":[137],"window.":[139]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}