{"id":"https://openalex.org/W4200052746","doi":"https://doi.org/10.1109/icfsp53514.2021.9646422","title":"HTS: High-Quality Training Set Selection for Pedestrian Detection","display_name":"HTS: High-Quality Training Set Selection for Pedestrian Detection","publication_year":2021,"publication_date":"2021-09-09","ids":{"openalex":"https://openalex.org/W4200052746","doi":"https://doi.org/10.1109/icfsp53514.2021.9646422"},"language":"en","primary_location":{"id":"doi:10.1109/icfsp53514.2021.9646422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icfsp53514.2021.9646422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 6th International Conference on Frontiers of Signal Processing (ICFSP)","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/A5068935408","display_name":"Junjie Li","orcid":"https://orcid.org/0000-0001-7349-8004"},"institutions":[{"id":"https://openalex.org/I918919364","display_name":"Switch","ror":"https://ror.org/02yw51758","country_code":"CH","type":"nonprofit","lineage":["https://openalex.org/I918919364"]},{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]},{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CH","CN"],"is_corresponding":true,"raw_author_name":"Junjie Li","raw_affiliation_strings":["State Key Laboratory of Networking &amp; Switching Technology, Beijing University of Posts &amp; Telecommunications, Beijing, China","State Key Laboratory of Networking &amp","Switching Technology, Beijing University of Posts &amp","Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Networking &amp; Switching Technology, Beijing University of Posts &amp; Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"State Key Laboratory of Networking &amp","institution_ids":[]},{"raw_affiliation_string":"Switching Technology, Beijing University of Posts &amp","institution_ids":["https://openalex.org/I139759216","https://openalex.org/I918919364"]},{"raw_affiliation_string":"Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I4210136246"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012633404","display_name":"Kai Shuang","orcid":"https://orcid.org/0000-0003-0917-3541"},"institutions":[{"id":"https://openalex.org/I918919364","display_name":"Switch","ror":"https://ror.org/02yw51758","country_code":"CH","type":"nonprofit","lineage":["https://openalex.org/I918919364"]},{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]},{"id":"https://openalex.org/I4210164386","display_name":"Hebei Science and Technology Department","ror":"https://ror.org/05k812a28","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210164386"]},{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]}],"countries":["CH","CN"],"is_corresponding":false,"raw_author_name":"Kai Shuang","raw_affiliation_strings":["Science and Technology on Communication Networks Laboratory, Shijiazhuang, P.R. China","State Key Laboratory of Networking &amp; Switching Technology, Beijing University of Posts &amp; Telecommunications, Beijing, China","Telecommunications, Beijing, China","Switching Technology, Beijing University of Posts &amp","State Key Laboratory of Networking &amp"],"affiliations":[{"raw_affiliation_string":"Science and Technology on Communication Networks Laboratory, Shijiazhuang, P.R. China","institution_ids":["https://openalex.org/I4210164386"]},{"raw_affiliation_string":"State Key Laboratory of Networking &amp; Switching Technology, Beijing University of Posts &amp; Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]},{"raw_affiliation_string":"Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I4210136246"]},{"raw_affiliation_string":"Switching Technology, Beijing University of Posts &amp","institution_ids":["https://openalex.org/I139759216","https://openalex.org/I918919364"]},{"raw_affiliation_string":"State Key Laboratory of Networking &amp","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008772211","display_name":"Wentao Zhang","orcid":"https://orcid.org/0000-0002-7532-5550"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wentao Zhang","raw_affiliation_strings":["AdTiming Technology Company Limited, Singapore"],"affiliations":[{"raw_affiliation_string":"AdTiming Technology Company Limited, Singapore","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068935408"],"corresponding_institution_ids":["https://openalex.org/I139759216","https://openalex.org/I4210136246","https://openalex.org/I918919364"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.16321895,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"58","last_page":"66"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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":0.9986000061035156,"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":0.8216364979743958},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.633644700050354},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5942599773406982},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5848054885864258},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5778666734695435},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5455403923988342},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.5391109585762024},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.5197743773460388},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5130240321159363},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.511971116065979},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5088139176368713},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4551210403442383},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.45179250836372375},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.44594210386276245},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.43301212787628174},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.38606250286102295},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07921552658081055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8216364979743958},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.633644700050354},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5942599773406982},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5848054885864258},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5778666734695435},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5455403923988342},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.5391109585762024},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.5197743773460388},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5130240321159363},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.511971116065979},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5088139176368713},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4551210403442383},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.45179250836372375},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.44594210386276245},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.43301212787628174},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.38606250286102295},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07921552658081055},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"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},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icfsp53514.2021.9646422","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icfsp53514.2021.9646422","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 6th International Conference on Frontiers of Signal Processing (ICFSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G2993257473","display_name":null,"funder_award_id":"61921003","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/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W2102605133","https://openalex.org/W2107775979","https://openalex.org/W2108598243","https://openalex.org/W2340897893","https://openalex.org/W2497039038","https://openalex.org/W2531915888","https://openalex.org/W2565639579","https://openalex.org/W2594507094","https://openalex.org/W2613599172","https://openalex.org/W2792824754","https://openalex.org/W2799199435","https://openalex.org/W2883363148","https://openalex.org/W2894820835","https://openalex.org/W2895077992","https://openalex.org/W2895451584","https://openalex.org/W2896540732","https://openalex.org/W2899607431","https://openalex.org/W2962721361","https://openalex.org/W2962850098","https://openalex.org/W2963037989","https://openalex.org/W2963318220","https://openalex.org/W2963351448","https://openalex.org/W2963516811","https://openalex.org/W2963681621","https://openalex.org/W2963769056","https://openalex.org/W2963998989","https://openalex.org/W2964241181","https://openalex.org/W2982770724","https://openalex.org/W2990075400","https://openalex.org/W2990130718","https://openalex.org/W3035524459","https://openalex.org/W3106250896","https://openalex.org/W6639102338","https://openalex.org/W6753388331","https://openalex.org/W6761662064","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2049003611","https://openalex.org/W2127804977","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Training":[0],"a":[1,35,39,57,71,97,106,112,138],"pedestrian":[2,164],"body":[3,10],"detector":[4],"generally":[5],"involves":[6],"the":[7,17,50,83,103,123,127,133,147,157],"supervision":[8,18,33],"of":[9,52,91,146],"bounding-boxes.":[11],"We":[12],"find":[13],"in":[14,105],"some":[15],"scenarios":[16],"is":[19,89],"not":[20],"clear":[21],"due":[22],"to":[23,37,101,132,143],"mixing":[24],"background":[25],"or":[26,179],"overlapping":[27],"persons.":[28],"Such":[29],"informative":[30],"but":[31],"noisy":[32,148],"imposes":[34],"challenge":[36],"learning":[38],"well-performed":[40],"detector.":[41],"In":[42],"this":[43,47],"work,":[44],"we":[45,95,110],"resolve":[46],"problem":[48],"from":[49,82],"perspective":[51],"data":[53,159],"selection":[54,62,160],"and":[55,80,170,181],"propose":[56],"learning-based":[58],"high-quality":[59],"training":[60,72,85,107,113,124,135],"set":[61,73,114,136],"strategy":[63,66,88,152,161],"(HTS).":[64],"Our":[65,87],"aims":[67],"at":[68],"selecting":[69],"(preprocessing)":[70],"with":[74],"best":[75,117],"trade-off":[76,99,104,118],"between":[77],"useful":[78],"information":[79],"noise":[81],"original":[84,134],"set.":[86,108,125],"composed":[90],"three":[92],"steps.":[93],"First":[94],"design":[96],"metric":[98],"quality":[100,119],"evaluate":[102],"Then":[109],"search":[111],"that":[115],"achieves":[116],"based":[120],"on":[121,166],"parameterizing":[122],"Finally":[126],"learned":[128],"parameters":[129],"are":[130],"applied":[131],"by":[137],"novel":[139],"re-weighting":[140],"scheme.":[141],"Due":[142],"improved":[144],"handling":[145],"data,":[149],"our":[150],"proposed":[151],"shows":[153],"consistent":[154],"superiority":[155],"over":[156],"typical":[158],"for":[162],"different":[163,167],"detectors":[165],"datasets":[168],"(Caltech":[169],"CityPersons":[171],"datasets)":[172],"without":[173],"any":[174],"specifically":[175],"designed":[176],"network":[177],"architecture":[178],"bells":[180],"whistles.":[182]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
