{"id":"https://openalex.org/W2512969578","doi":"https://doi.org/10.1109/ivs.2016.7535405","title":"Robust pedestrian attribute recognition for an unbalanced dataset using mini-batch training with rarity rate","display_name":"Robust pedestrian attribute recognition for an unbalanced dataset using mini-batch training with rarity rate","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2512969578","doi":"https://doi.org/10.1109/ivs.2016.7535405","mag":"2512969578"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2016.7535405","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","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/A5031685667","display_name":"Hiroshi Fukui","orcid":"https://orcid.org/0000-0002-7880-635X"},"institutions":[{"id":"https://openalex.org/I184937672","display_name":"Chubu University","ror":"https://ror.org/02sps0775","country_code":"JP","type":"education","lineage":["https://openalex.org/I184937672"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Fukui","raw_affiliation_strings":["Chubu University, MatSUMOto cho, Kasugai, Aichi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chubu University, MatSUMOto cho, Kasugai, Aichi, Japan","institution_ids":["https://openalex.org/I184937672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062009349","display_name":"Takayoshi Yamashita","orcid":"https://orcid.org/0000-0003-2631-9856"},"institutions":[{"id":"https://openalex.org/I184937672","display_name":"Chubu University","ror":"https://ror.org/02sps0775","country_code":"JP","type":"education","lineage":["https://openalex.org/I184937672"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takayoshi Yamashita","raw_affiliation_strings":["Chubu University, MatSUMOto cho, Kasugai, Aichi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chubu University, MatSUMOto cho, Kasugai, Aichi, Japan","institution_ids":["https://openalex.org/I184937672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102888926","display_name":"Yuji Yamauchi","orcid":"https://orcid.org/0000-0002-6573-5855"},"institutions":[{"id":"https://openalex.org/I184937672","display_name":"Chubu University","ror":"https://ror.org/02sps0775","country_code":"JP","type":"education","lineage":["https://openalex.org/I184937672"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuji Yamauchi","raw_affiliation_strings":["Chubu University, MatSUMOto cho, Kasugai, Aichi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chubu University, MatSUMOto cho, Kasugai, Aichi, Japan","institution_ids":["https://openalex.org/I184937672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011810133","display_name":"Hironobu Fujiyoshi","orcid":"https://orcid.org/0000-0001-7391-4725"},"institutions":[{"id":"https://openalex.org/I184937672","display_name":"Chubu University","ror":"https://ror.org/02sps0775","country_code":"JP","type":"education","lineage":["https://openalex.org/I184937672"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hironobu Fujiyoshi","raw_affiliation_strings":["Chubu University, MatSUMOto cho, Kasugai, Aichi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chubu University, MatSUMOto cho, Kasugai, Aichi, Japan","institution_ids":["https://openalex.org/I184937672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085309296","display_name":"Hiroshi Murase","orcid":"https://orcid.org/0000-0002-8103-9294"},"institutions":[{"id":"https://openalex.org/I60134161","display_name":"Nagoya University","ror":"https://ror.org/04chrp450","country_code":"JP","type":"education","lineage":["https://openalex.org/I60134161"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroshi Murase","raw_affiliation_strings":["Nagoya University, Furo cho, Chikusa ku, Nagoya, Aichi, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nagoya University, Furo cho, Chikusa ku, Nagoya, Aichi, Japan","institution_ids":["https://openalex.org/I60134161"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8451,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.80776066,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"322","last_page":"327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"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":0.9993000030517578,"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"}},{"id":"https://openalex.org/T12406","display_name":"IoT and GPS-based Vehicle Safety Systems","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical 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/computer-science","display_name":"Computer science","score":0.7975124716758728},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7023029923439026},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6543058156967163},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.637017548084259},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5776128768920898},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5451111793518066},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5433458089828491},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.517062246799469},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.5028616786003113},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.41892021894454956},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.4134889543056488},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36350059509277344},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08824911713600159}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7975124716758728},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7023029923439026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6543058156967163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.637017548084259},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5776128768920898},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5451111793518066},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5433458089828491},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.517062246799469},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.5028616786003113},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.41892021894454956},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.4134889543056488},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36350059509277344},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08824911713600159},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"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/ivs.2016.7535405","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2016.7535405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320338121","display_name":"Center of Innovation Program","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1499780422","https://openalex.org/W1896424170","https://openalex.org/W1976818984","https://openalex.org/W2031454541","https://openalex.org/W2036554549","https://openalex.org/W2065180801","https://openalex.org/W2078224158","https://openalex.org/W2092135080","https://openalex.org/W2115471590","https://openalex.org/W2120419212","https://openalex.org/W2121955477","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2228074482","https://openalex.org/W3125261624","https://openalex.org/W4312531999","https://openalex.org/W6673819590","https://openalex.org/W6676970727","https://openalex.org/W6678178411"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W2565999991","https://openalex.org/W3046775127","https://openalex.org/W3107474891","https://openalex.org/W2966207284","https://openalex.org/W2385744493","https://openalex.org/W3099765033","https://openalex.org/W27633560"],"abstract_inverted_index":{"Pedestrian":[0,10],"attributes":[1,11],"are":[2,21,177],"significant":[3],"information":[4,31],"for":[5,37,115,136,170,208],"Advanced":[6],"Driver":[7],"Assistance":[8],"System(ADAS).":[9],"such":[12,137],"as":[13],"body":[14],"poses,":[15],"face":[16],"orientations":[17],"and":[18,94,101,148],"open":[19,211],"umbrella":[20],"meant":[22],"action":[23],"or":[24,70],"state":[25],"of":[26,42,62,133,151],"pedestrian.":[27],"In":[28,53,84,122],"general,":[29],"this":[30,66,123,180],"is":[32,46,73,88,158],"recognized":[33],"using":[34],"independent":[35],"classifiers":[36],"each":[38,152,161],"task.":[39,153],"Performing":[40],"all":[41],"these":[43],"separate":[44],"tasks":[45],"too":[47],"time-consuming":[48],"at":[49],"the":[50,55,60,131,146,165,186,197],"testing":[51,102],"stage.":[52],"addition,":[54],"processing":[56],"time":[57],"increases":[58],"with":[59,117,188,196],"number":[61],"tasks.":[63,83],"To":[64],"address":[65],"problem,":[67],"multi-task":[68],"learning":[69,72,87,106,135,194],"heterogeneous":[71,86,105,134,193],"able":[74,89],"to":[75,80,90,108,129,160,183],"train":[76,92],"a":[77,111,118,127,141,168,172,189],"single":[78],"classifier":[79],"perform":[81],"multiple":[82],"particular,":[85],"simultaneously":[91],"regression":[93],"recognition":[95],"tasks,":[96],"because":[97],"reducing":[98],"both":[99],"training":[100,120,162,171],"time.":[103],"However,":[104],"tends":[107],"result":[109],"in":[110,167],"lower":[112],"accuracy":[113],"rate":[114,143,157,182,199],"classes":[116,209],"few":[119,190],"samples.":[121,191],"paper,":[124],"we":[125],"propose":[126],"method":[128],"improve":[130],"performance":[132,202],"classes.":[138],"We":[139],"introduce":[140],"rarity":[142,156,181,198],"based":[144],"on":[145,185,203],"importance":[147],"class":[149,187],"probability":[150],"The":[154],"appropriate":[155],"assigned":[159],"sample.":[163],"Thus,":[164],"samples":[166],"mini-batch":[169],"deep":[173],"convolutional":[174],"neural":[175],"network":[176],"augmented":[178],"by":[179],"focus":[184],"Our":[192],"approach":[195],"attains":[200],"better":[201],"pedestrian":[204],"attribute":[205],"recognition,":[206],"especially":[207],"representing":[210],"umbrellas.":[212]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}