{"id":"https://openalex.org/W2909235154","doi":"https://doi.org/10.1145/3290589.3290601","title":"Pedestrian Detection based on Reduced High-Dimensional Distinctive Feature using Deep Neural Network","display_name":"Pedestrian Detection based on Reduced High-Dimensional Distinctive Feature using Deep Neural Network","publication_year":2018,"publication_date":"2018-10-12","ids":{"openalex":"https://openalex.org/W2909235154","doi":"https://doi.org/10.1145/3290589.3290601","mag":"2909235154"},"language":"en","primary_location":{"id":"doi:10.1145/3290589.3290601","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3290589.3290601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 International Conference on Sensors, Signal and Image Processing","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/A5020338294","display_name":"Hyun Chul Song","orcid":null},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyun Chul Song","raw_affiliation_strings":["Department of Computer, Science and Engineering, Chung-Ang University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer, Science and Engineering, Chung-Ang University, Seoul, Korea","institution_ids":["https://openalex.org/I67900169"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038058440","display_name":"Kwang Nam Choi","orcid":"https://orcid.org/0000-0002-7420-9216"},"institutions":[{"id":"https://openalex.org/I67900169","display_name":"Chung-Ang University","ror":"https://ror.org/01r024a98","country_code":"KR","type":"education","lineage":["https://openalex.org/I67900169"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kwang Nam Choi","raw_affiliation_strings":["Department of Computer, Science and Engineering, Chung-Ang University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer, Science and Engineering, Chung-Ang University, Seoul, Korea","institution_ids":["https://openalex.org/I67900169"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16571887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"6","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9994999766349792,"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":0.9987999796867371,"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/computer-science","display_name":"Computer science","score":0.7790390849113464},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6752009987831116},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6697772741317749},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.6142086982727051},{"id":"https://openalex.org/keywords/optimal-distinctiveness-theory","display_name":"Optimal distinctiveness theory","score":0.561726450920105},{"id":"https://openalex.org/keywords/bag-of-words-model-in-computer-vision","display_name":"Bag-of-words model in computer vision","score":0.5524529218673706},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5188835263252258},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4962678551673889},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4920656085014343},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.439848393201828},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.42226290702819824},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41975387930870056},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.41342583298683167},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3685031235218048},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3071171045303345},{"id":"https://openalex.org/keywords/visual-word","display_name":"Visual Word","score":0.26363107562065125},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.23324793577194214},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07184615731239319}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7790390849113464},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6752009987831116},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6697772741317749},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.6142086982727051},{"id":"https://openalex.org/C47385372","wikidata":"https://www.wikidata.org/wiki/Q7098943","display_name":"Optimal distinctiveness theory","level":2,"score":0.561726450920105},{"id":"https://openalex.org/C167611913","wikidata":"https://www.wikidata.org/wiki/Q6884747","display_name":"Bag-of-words model in computer vision","level":5,"score":0.5524529218673706},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5188835263252258},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4962678551673889},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4920656085014343},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.439848393201828},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.42226290702819824},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41975387930870056},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.41342583298683167},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3685031235218048},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3071171045303345},{"id":"https://openalex.org/C189391414","wikidata":"https://www.wikidata.org/wiki/Q7936579","display_name":"Visual Word","level":4,"score":0.26363107562065125},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.23324793577194214},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07184615731239319},{"id":"https://openalex.org/C542102704","wikidata":"https://www.wikidata.org/wiki/Q183257","display_name":"Psychotherapist","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},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3290589.3290601","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3290589.3290601","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 International Conference on Sensors, Signal and Image Processing","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":0.75}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W280632315","https://openalex.org/W1576445103","https://openalex.org/W1625255723","https://openalex.org/W1665214252","https://openalex.org/W1728842521","https://openalex.org/W1924689489","https://openalex.org/W1977295328","https://openalex.org/W2096152098","https://openalex.org/W2102356926","https://openalex.org/W2103658758","https://openalex.org/W2104978738","https://openalex.org/W2107034620","https://openalex.org/W2111931125","https://openalex.org/W2112020727","https://openalex.org/W2124285714","https://openalex.org/W2124386111","https://openalex.org/W2131846894","https://openalex.org/W2133001599","https://openalex.org/W2147238549","https://openalex.org/W2154683974","https://openalex.org/W2159625953","https://openalex.org/W2162915993","https://openalex.org/W2166049352","https://openalex.org/W2171706135","https://openalex.org/W2172191903","https://openalex.org/W2546302380","https://openalex.org/W4248702647","https://openalex.org/W6603376977"],"related_works":["https://openalex.org/W4225552138","https://openalex.org/W2032875729","https://openalex.org/W3046185751","https://openalex.org/W2477273251","https://openalex.org/W2475308993","https://openalex.org/W2790526747","https://openalex.org/W2069575426","https://openalex.org/W3002212064","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1],"is":[2,22,86,125,137,181],"an":[3,175],"essential":[4],"and":[5,154,170],"significant":[6],"research":[7],"topic":[8],"due":[9],"to":[10,69],"its":[11],"diverse":[12],"applications":[13],"in":[14,91,167],"the":[15,25,43,56,70,75,129,140,143,148,161,186],"area":[16],"of":[17,45,58,61,77,103,127,131,178],"safety":[18],"systems.":[19],"The":[20,80,134,156],"distinctiveness":[21,76],"detected":[23],"by":[24,33,41],"following":[26,149],"three":[27],"steps.":[28],"Firstly,":[29],"images":[30],"are":[31,39,53,67],"represented":[32],"their":[34,51],"respective":[35],"co-occurrence":[36],"matrices,":[37],"which":[38,94,180],"vectorized":[40],"using":[42,142],"bag":[44],"visual":[46,59,78,113],"words":[47,60],"(BoVW)":[48],"framework.":[49],"Secondly,":[50],"weights":[52,66],"calculated":[54],"from":[55],"histograms":[57],"each":[62],"class.":[63],"Finally,":[64],"computed":[65],"applied":[68],"testing":[71],"image":[72],"set":[73],"as":[74,88,118],"words.":[79],"fully":[81],"connected":[82],"Multi-Layer":[83],"Perceptron":[84],"(MLP)":[85],"used":[87],"classification":[89],"method":[90,136,163],"our":[92],"system,":[93],"has":[95],"limited":[96],"performance.":[97],"This":[98],"paper":[99],"introduces":[100],"a":[101,119],"combination":[102],"principle":[104],"component":[105],"analysis":[106],"(PCA),":[107],"guided":[108],"filtering,":[109],"deeplearning":[110],"architecture":[111],"into":[112],"data":[114],"classification.":[115],"In":[116],"detail,":[117],"mature":[120],"dimension":[121],"reduction":[122],"architecture,":[123],"PCA":[124],"capable":[126],"reducing":[128],"redundancy":[130],"multi-dimensional":[132],"information.":[133],"proposed":[135,162],"compared":[138,187],"with":[139,147],"MLP":[141],"Caltech":[144],"256":[145],"datasets,":[146],"classes:":[150],"pedestrians,":[151],"cars,":[152],"motorbikes":[153],"airplanes.":[155],"experimental":[157],"results":[158],"show":[159],"that":[160],"outperforms":[164],"current":[165],"methods":[166],"predicting":[168],"pedestrians":[169],"transportation":[171],"objects.":[172],"It":[173],"yields":[174],"average":[176],"accuracy":[177],"96.50%,":[179],"approximately":[182],"8.5%":[183],"more":[184],"than":[185],"state-of-the-art.":[188]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}