{"id":"https://openalex.org/W2040898095","doi":"https://doi.org/10.1109/icip.2010.5650143","title":"Improving person detection using synthetic training data","display_name":"Improving person detection using synthetic training data","publication_year":2010,"publication_date":"2010-09-01","ids":{"openalex":"https://openalex.org/W2040898095","doi":"https://doi.org/10.1109/icip.2010.5650143","mag":"2040898095"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2010.5650143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2010.5650143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on 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/A5100780327","display_name":"Jie Yu","orcid":"https://orcid.org/0000-0002-4016-1349"},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jie Yu","raw_affiliation_strings":["Corporate Research Advance Engineering Multimedia, Robert Bosch GmbH, Hildesheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Research Advance Engineering Multimedia, Robert Bosch GmbH, Hildesheim, Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108362750","display_name":"Dirk Farin","orcid":null},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dirk Farin","raw_affiliation_strings":["Corporate Research Advance Engineering Multimedia, Robert Bosch GmbH, Hildesheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Research Advance Engineering Multimedia, Robert Bosch GmbH, Hildesheim, Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000284342","display_name":"Christof Kruger","orcid":null},"institutions":[{"id":"https://openalex.org/I889804353","display_name":"Robert Bosch (Germany)","ror":"https://ror.org/01fe0jt45","country_code":"DE","type":"company","lineage":["https://openalex.org/I889804353"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christof Kruger","raw_affiliation_strings":["Corporate Research Advance Engineering Multimedia, Robert Bosch GmbH, Hildesheim, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Research Advance Engineering Multimedia, Robert Bosch GmbH, Hildesheim, Germany","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051534545","display_name":"Bernt Schiele","orcid":"https://orcid.org/0000-0001-9683-5237"},"institutions":[{"id":"https://openalex.org/I31512782","display_name":"Technische Universit\u00e4t Darmstadt","ror":"https://ror.org/05n911h24","country_code":"DE","type":"education","lineage":["https://openalex.org/I31512782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bernt Schiele","raw_affiliation_strings":["Computer Science Department, TU Darmstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Computer Science Department, TU Darmstadt, Germany","institution_ids":["https://openalex.org/I31512782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3229,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.59713906,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"2","issue":null,"first_page":"3477","last_page":"3480"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":1.0,"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":1.0,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9991000294685364,"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":0.9975000023841858,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7679263353347778},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7543345093727112},{"id":"https://openalex.org/keywords/adaboost","display_name":"AdaBoost","score":0.7251299619674683},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6152887344360352},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5690833926200867},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5638965368270874},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5554997324943542},{"id":"https://openalex.org/keywords/real-world-data","display_name":"Real world data","score":0.5371177196502686},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4910616874694824},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4725582003593445},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46213996410369873},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.432680606842041},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22120574116706848},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.1603160798549652}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7679263353347778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7543345093727112},{"id":"https://openalex.org/C141404830","wikidata":"https://www.wikidata.org/wiki/Q2823869","display_name":"AdaBoost","level":3,"score":0.7251299619674683},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6152887344360352},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5690833926200867},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5638965368270874},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5554997324943542},{"id":"https://openalex.org/C3020493868","wikidata":"https://www.wikidata.org/wiki/Q55631277","display_name":"Real world data","level":2,"score":0.5371177196502686},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4910616874694824},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4725582003593445},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46213996410369873},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.432680606842041},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22120574116706848},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1603160798549652},{"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/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2010.5650143","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2010.5650143","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2010 IEEE International Conference on Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1529681343","https://openalex.org/W1581372780","https://openalex.org/W1585192441","https://openalex.org/W1906375339","https://openalex.org/W2108301944","https://openalex.org/W2143023037","https://openalex.org/W2161969291","https://openalex.org/W2337904331","https://openalex.org/W3097096317","https://openalex.org/W3214102110","https://openalex.org/W6631539407","https://openalex.org/W6634698118","https://openalex.org/W6639934059","https://openalex.org/W6676174793","https://openalex.org/W6704028418"],"related_works":["https://openalex.org/W4221160360","https://openalex.org/W4312659495","https://openalex.org/W3208934527","https://openalex.org/W4385366257","https://openalex.org/W3101007570","https://openalex.org/W4387910575","https://openalex.org/W3176425421","https://openalex.org/W3091312527","https://openalex.org/W1555087354","https://openalex.org/W3170935366"],"abstract_inverted_index":{"Person":[0],"detection":[1,26,45,100],"in":[2,63],"complex":[3],"real-world":[4,49,78],"scenes":[5,65,83],"is":[6,32],"a":[7,40],"challenging":[8],"problem.":[9],"State-of-the-art":[10],"methods":[11],"typically":[12],"use":[13],"supervised":[14],"learning":[15],"relying":[16],"on":[17,77],"significant":[18,85],"amounts":[19],"of":[20,60,81,91],"training":[21,30,54,95],"data":[22,31,50,96],"to":[23,43],"achieve":[24],"good":[25],"results.":[27,101],"However,":[28],"labeling":[29],"tedious,":[33],"expensive,":[34],"and":[35,71,93],"error-prone.":[36],"This":[37],"paper":[38],"presents":[39],"novel":[41],"method":[42],"improve":[44],"performance":[46],"by":[47],"supplementing":[48],"with":[51,84],"synthetically":[52],"generated":[53],"data.":[55],"We":[56],"consider":[57],"the":[58,89],"case":[59],"detecting":[61],"people":[62],"crowded":[64,82],"within":[66],"an":[67],"AdaBoost-framework":[68],"employing":[69],"Haar":[70],"Histogram-of-Oriented-Gradients":[72],"(HOG)":[73],"features.":[74],"Our":[75],"evaluations":[76],"video":[79],"sequences":[80],"occlusions":[86],"show":[87],"that":[88],"combination":[90],"real":[92],"synthetic":[94],"significantly":[97],"improves":[98],"overall":[99]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2},{"year":2015,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
