{"id":"https://openalex.org/W1553770567","doi":"https://doi.org/10.1109/icassp.2015.7178199","title":"Pedestrian detection via PCA filters based convolutional channel features","display_name":"Pedestrian detection via PCA filters based convolutional channel features","publication_year":2015,"publication_date":"2015-04-01","ids":{"openalex":"https://openalex.org/W1553770567","doi":"https://doi.org/10.1109/icassp.2015.7178199","mag":"1553770567"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2015.7178199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2015.7178199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5101867597","display_name":"Wei Ke","orcid":"https://orcid.org/0000-0002-2899-0371"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Ke","raw_affiliation_strings":["School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100446997","display_name":"Yao Zhang","orcid":"https://orcid.org/0000-0002-8759-4811"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yao Zhang","raw_affiliation_strings":["School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041227759","display_name":"Pengxu Wei","orcid":"https://orcid.org/0000-0002-2190-0767"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengxu Wei","raw_affiliation_strings":["School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015317495","display_name":"Qixiang Ye","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qixiang Ye","raw_affiliation_strings":["School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110088325","display_name":"Jianbin Jiao","orcid":"https://orcid.org/0000-0003-0454-3929"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianbin Jiao","raw_affiliation_strings":["School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronics Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]},{"raw_affiliation_string":"School of Electronics, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1394","last_page":"1398"},"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":0.9995999932289124,"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":0.9969000220298767,"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-detection","display_name":"Pedestrian detection","score":0.744827151298523},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6984347105026245},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6920394897460938},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6782307624816895},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6439330577850342},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6284534931182861},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.5360638499259949},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.493918240070343},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.47732383012771606},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37742772698402405},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.35515502095222473},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17625516653060913},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.1611785888671875}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.744827151298523},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6984347105026245},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6920394897460938},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6782307624816895},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6439330577850342},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6284534931182861},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.5360638499259949},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.493918240070343},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.47732383012771606},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37742772698402405},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.35515502095222473},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17625516653060913},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.1611785888671875},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2015.7178199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2015.7178199","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.6100000143051147,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W7746136","https://openalex.org/W1536943225","https://openalex.org/W1616262590","https://openalex.org/W1705975472","https://openalex.org/W1999853363","https://openalex.org/W2008552202","https://openalex.org/W2010181071","https://openalex.org/W2027642505","https://openalex.org/W2031454541","https://openalex.org/W2034779469","https://openalex.org/W2051596736","https://openalex.org/W2077513643","https://openalex.org/W2088049833","https://openalex.org/W2097324787","https://openalex.org/W2116022929","https://openalex.org/W2117687030","https://openalex.org/W2121102817","https://openalex.org/W2122853526","https://openalex.org/W2125556102","https://openalex.org/W2133755669","https://openalex.org/W2136724559","https://openalex.org/W2139479830","https://openalex.org/W2151049637","https://openalex.org/W2159386181","https://openalex.org/W2161106546","https://openalex.org/W2161969291","https://openalex.org/W2162741153","https://openalex.org/W2168356304","https://openalex.org/W2169671170","https://openalex.org/W2276617118","https://openalex.org/W2548197316","https://openalex.org/W3097096317","https://openalex.org/W3102431071","https://openalex.org/W6600313631","https://openalex.org/W6632204412","https://openalex.org/W6637230477","https://openalex.org/W6674662834","https://openalex.org/W6678163432"],"related_works":["https://openalex.org/W2392100589","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/W2913302899","https://openalex.org/W3204901196"],"abstract_inverted_index":{"In":[0,42],"this":[1],"paper,":[2],"we":[3,82],"propose":[4],"a":[5,84],"kind":[6],"of":[7,64],"image":[8],"representation,":[9],"named":[10],"PCA":[11,32,57],"filters":[12,33,58],"based":[13],"convolutional":[14,27,45],"channel":[15,39],"features":[16,40,63,77],"(PCA-CCF)":[17],"for":[18],"pedestrian":[19,52,86,109],"detection.":[20],"The":[21,55],"motivation":[22],"is":[23],"to":[24,34,51],"use":[25],"the":[26,36,44,48,60,74,102],"network":[28],"architecture":[29],"with":[30],"orthogonal":[31],"enhance":[35],"state-of-the-art":[37],"aggregate":[38],"(ACF).":[41],"PCA-CCF,":[43],"operation":[46],"improves":[47],"feature":[49,70],"robustness":[50],"local":[53],"deformation.":[54],"learned":[56],"reduce":[59],"correlations":[61],"among":[62],"each":[65],"channel,":[66],"and":[67,78,97,106],"therefore,":[68],"improve":[69],"discrimination":[71],"capability.":[72],"With":[73],"proposed":[75],"PCA-CCF":[76],"cascaded":[79],"AdaBoost":[80],"classifiers,":[81],"develop":[83],"coarse-to-fine":[85],"detection":[87],"approach.":[88],"Experiments":[89],"show":[90],"that":[91],"such":[92],"approach":[93],"achieves":[94],"3.04%,":[95],"17.87%":[96],"6.28%":[98],"performance":[99],"gain":[100],"on":[101],"INRIA,":[103],"Caltech":[104,107],"Reasonable":[105],"Overall":[108],"datasets,":[110],"respectively.":[111]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4},{"year":2017,"cited_by_count":2},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}