{"id":"https://openalex.org/W2530479064","doi":"https://doi.org/10.1109/tits.2016.2612705","title":"Local Co-Occurrence Selection via Partial Least Squares for Pedestrian Detection","display_name":"Local Co-Occurrence Selection via Partial Least Squares for Pedestrian Detection","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2530479064","doi":"https://doi.org/10.1109/tits.2016.2612705","mag":"2530479064"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2016.2612705","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2016.2612705","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5100763663","display_name":"Qiming Li","orcid":"https://orcid.org/0000-0001-7289-0901"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiming Li","raw_affiliation_strings":["Department of Computer Science, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044594971","display_name":"Hanzi Wang","orcid":"https://orcid.org/0000-0002-6913-9786"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanzi Wang","raw_affiliation_strings":["Department of Computer Science, Xiamen University, Xiamen, China"],"raw_orcid":"https://orcid.org/0000-0002-6913-9786","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100395059","display_name":"Yan Yan","orcid":"https://orcid.org/0000-0002-3674-7160"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Yan","raw_affiliation_strings":["Department of Computer Science, Xiamen University, Xiamen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Xiamen University, Xiamen, China","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100743524","display_name":"Bo Li","orcid":"https://orcid.org/0000-0003-3799-2018"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Li","raw_affiliation_strings":["Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of Digital Media, School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002277899","display_name":"Chang Wen Chen","orcid":"https://orcid.org/0000-0002-6720-234X"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chang Wen Chen","raw_affiliation_strings":["University at Buffalo, The State University of New York, Buffalo, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo, The State University of New York, Buffalo, NY, USA","institution_ids":["https://openalex.org/I63190737"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3315,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.66661023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","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":0.9998999834060669,"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":0.9991999864578247,"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":0.9973000288009644,"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/feature-selection","display_name":"Feature selection","score":0.7996900677680969},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7184481620788574},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6746006608009338},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6733939051628113},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6618071794509888},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6593428254127502},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6487873792648315},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5790896415710449},{"id":"https://openalex.org/keywords/local-binary-patterns","display_name":"Local binary patterns","score":0.510852038860321},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.490610271692276},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.48247793316841125},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43064698576927185},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.409845769405365},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.3607028126716614},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.3344077169895172},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3190126121044159},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14794570207595825}],"concepts":[{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7996900677680969},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7184481620788574},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6746006608009338},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6733939051628113},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6618071794509888},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6593428254127502},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6487873792648315},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5790896415710449},{"id":"https://openalex.org/C87335442","wikidata":"https://www.wikidata.org/wiki/Q2494345","display_name":"Local binary patterns","level":4,"score":0.510852038860321},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.490610271692276},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.48247793316841125},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43064698576927185},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.409845769405365},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.3607028126716614},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.3344077169895172},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3190126121044159},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14794570207595825},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2016.2612705","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2016.2612705","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7400000095367432,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G2930454911","display_name":"\u9c81\u68d2\u6a21\u578b\u62df\u5408\u4e2d\u7684\u5173\u952e\u95ee\u9898\u7814\u7a76\u53ca\u5e94\u7528","funder_award_id":"61472334","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3313464985","display_name":"\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u4eba\u8138\u8bc6\u522b\u5173\u952e\u6280\u672f\u7814\u7a76","funder_award_id":"61571379","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":39,"referenced_works":["https://openalex.org/W1526734559","https://openalex.org/W1650122911","https://openalex.org/W1899216531","https://openalex.org/W1991031523","https://openalex.org/W2010340098","https://openalex.org/W2031454541","https://openalex.org/W2034779469","https://openalex.org/W2042949540","https://openalex.org/W2053544201","https://openalex.org/W2074777933","https://openalex.org/W2077513643","https://openalex.org/W2081021369","https://openalex.org/W2084997728","https://openalex.org/W2098064689","https://openalex.org/W2104606180","https://openalex.org/W2110226160","https://openalex.org/W2111662190","https://openalex.org/W2113635748","https://openalex.org/W2117687030","https://openalex.org/W2122853526","https://openalex.org/W2125556102","https://openalex.org/W2127420331","https://openalex.org/W2137225583","https://openalex.org/W2140090057","https://openalex.org/W2151049637","https://openalex.org/W2151454023","https://openalex.org/W2156547346","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2162741153","https://openalex.org/W2170101770","https://openalex.org/W2200528286","https://openalex.org/W2534262995","https://openalex.org/W2953300457","https://openalex.org/W6636787326","https://openalex.org/W6639725375","https://openalex.org/W6674977583","https://openalex.org/W6675436802","https://openalex.org/W6684811926"],"related_works":["https://openalex.org/W2055219403","https://openalex.org/W2791313072","https://openalex.org/W3166997759","https://openalex.org/W2965546495","https://openalex.org/W2583894904","https://openalex.org/W2972620127","https://openalex.org/W1990254706","https://openalex.org/W2404514746","https://openalex.org/W1843372508","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Channel":[0],"feature":[1,26,70,98,106,113,146],"detectors":[2],"are":[3,158,169],"the":[4,18,35,50,76,85,92,97,118,122,130,137,172],"most":[5,13,138],"popular":[6],"approaches":[7,16],"for":[8,60],"pedestrian":[9,61,163,180],"detection":[10,181],"recently.":[11],"However,":[12,105],"of":[14,52,69,132],"these":[15],"train":[17],"boosted":[19],"decision":[20],"trees":[21],"by":[22,74,90,171],"selecting":[23,91],"a":[24,66,101,142],"single":[25],"at":[27,121],"each":[28,81],"node,":[29],"which":[30,115],"does":[31],"not":[32],"effectively":[33],"exploit":[34],"multi-feature":[36],"cues":[37],"and":[38,84,135,166],"spatial":[39,86],"information.":[40],"To":[41],"address":[42],"this":[43,45],"issue,":[44],"paper":[46],"proposes":[47],"to":[48,94,110,128],"construct":[49],"co-occurrence":[51,71,99,107],"multiple":[53],"channel":[54,82],"features":[55,134,140],"in":[56,100,126,175],"local":[57,102],"image":[58,103],"neighborhoods":[59],"detection.":[62],"In":[63],"our":[64],"approach,":[65],"binary":[67,77],"pattern":[68],"is":[72,88,154],"represented":[73],"combining":[75],"variables":[78],"quantized":[79],"from":[80],"feature,":[83],"information":[87],"incorporated":[89],"neighbors":[93],"jointly":[95],"represent":[96],"block.":[104],"selection":[108,147],"leads":[109],"many":[111],"possible":[112],"combinations,":[114],"significantly":[116],"increase":[117],"computational":[119],"cost":[120],"training":[123],"stage.":[124],"Therefore,":[125],"order":[127],"reduce":[129],"number":[131],"candidate":[133],"obtain":[136],"discriminative":[139],"effectively,":[141],"partial":[143],"least":[144],"squares-based":[145],"approach":[148,174],"called":[149],"variable":[150],"importance":[151],"on":[152,160],"projection":[153],"exploited.":[155],"Comprehensive":[156],"experiments":[157],"conducted":[159],"several":[161],"challenging":[162],"data":[164],"sets,":[165],"superior":[167],"performances":[168],"achieved":[170],"proposed":[173],"comparison":[176],"with":[177],"some":[178],"state-of-the-art":[179],"approaches.":[182]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}