{"id":"https://openalex.org/W2890264872","doi":"https://doi.org/10.1109/aipr.2017.8457936","title":"Automated generation of convolutional neural network training data using video sources","display_name":"Automated generation of convolutional neural network training data using video sources","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2890264872","doi":"https://doi.org/10.1109/aipr.2017.8457936","mag":"2890264872"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2017.8457936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5023672303","display_name":"Andrew Kalukin","orcid":"https://orcid.org/0000-0001-5644-7096"},"institutions":[{"id":"https://openalex.org/I1319259000","display_name":"National Geospatial-Intelligence Agency","ror":"https://ror.org/02k4pxv54","country_code":"US","type":"government","lineage":["https://openalex.org/I1319259000","https://openalex.org/I1330347796"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew R. Kalukin","raw_affiliation_strings":["National Geospatial-Intelligence Agency, Springfield, VA, USA"],"affiliations":[{"raw_affiliation_string":"National Geospatial-Intelligence Agency, Springfield, VA, USA","institution_ids":["https://openalex.org/I1319259000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042236966","display_name":"Wade Leonard","orcid":null},"institutions":[{"id":"https://openalex.org/I1319259000","display_name":"National Geospatial-Intelligence Agency","ror":"https://ror.org/02k4pxv54","country_code":"US","type":"government","lineage":["https://openalex.org/I1319259000","https://openalex.org/I1330347796"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wade Leonard","raw_affiliation_strings":["National Geospatial-Intelligence Agency, Springfield, VA, USA"],"affiliations":[{"raw_affiliation_string":"National Geospatial-Intelligence Agency, Springfield, VA, USA","institution_ids":["https://openalex.org/I1319259000"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074936902","display_name":"Joan Green","orcid":null},"institutions":[{"id":"https://openalex.org/I1319259000","display_name":"National Geospatial-Intelligence Agency","ror":"https://ror.org/02k4pxv54","country_code":"US","type":"government","lineage":["https://openalex.org/I1319259000","https://openalex.org/I1330347796"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joan Green","raw_affiliation_strings":["National Geospatial-Intelligence Agency, Springfield, VA, USA"],"affiliations":[{"raw_affiliation_string":"National Geospatial-Intelligence Agency, Springfield, VA, USA","institution_ids":["https://openalex.org/I1319259000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087965907","display_name":"Lester Burgwardt","orcid":null},"institutions":[{"id":"https://openalex.org/I1319259000","display_name":"National Geospatial-Intelligence Agency","ror":"https://ror.org/02k4pxv54","country_code":"US","type":"government","lineage":["https://openalex.org/I1319259000","https://openalex.org/I1330347796"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lester Burgwardt","raw_affiliation_strings":["National Geospatial-Intelligence Agency, Springfield, VA, USA"],"affiliations":[{"raw_affiliation_string":"National Geospatial-Intelligence Agency, Springfield, VA, USA","institution_ids":["https://openalex.org/I1319259000"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5023672303"],"corresponding_institution_ids":["https://openalex.org/I1319259000"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.25055634,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"98280e","issue":null,"first_page":"1","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","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/T10812","display_name":"Human Pose and Action Recognition","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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9973999857902527,"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":0.9973999857902527,"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":0.8684679269790649},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7733801603317261},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7570333480834961},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.7291585206985474},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6142446398735046},{"id":"https://openalex.org/keywords/frame-rate","display_name":"Frame rate","score":0.546983003616333},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5448825359344482},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5148429274559021},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.47662675380706787},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4550631046295166},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4513770341873169},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.43798360228538513},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4116789698600769},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3230457007884979}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8684679269790649},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7733801603317261},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7570333480834961},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.7291585206985474},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6142446398735046},{"id":"https://openalex.org/C3261483","wikidata":"https://www.wikidata.org/wiki/Q119565","display_name":"Frame rate","level":2,"score":0.546983003616333},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5448825359344482},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5148429274559021},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.47662675380706787},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4550631046295166},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4513770341873169},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.43798360228538513},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4116789698600769},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3230457007884979},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr.2017.8457936","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457936","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.5199999809265137,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2016053056","https://openalex.org/W2142194269","https://openalex.org/W2398813871","https://openalex.org/W2962790054","https://openalex.org/W6654335291","https://openalex.org/W6680835844","https://openalex.org/W6712742248","https://openalex.org/W6736168322"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2969228573","https://openalex.org/W2977517636","https://openalex.org/W4310880131","https://openalex.org/W2810129309"],"abstract_inverted_index":{"One":[0,77],"of":[1,4,31,47,49,70,86,104,106,131,147,171],"the":[2,93,102,126,138,195],"challenges":[3],"using":[5,116,159],"techniques":[6],"such":[7,73,118],"as":[8,83,119,135,143],"convolutional":[9],"neural":[10],"networks":[11],"and":[12,21,56,95,137,186],"deep":[13],"learning":[14],"for":[15,51,101,129],"automated":[16],"object":[17],"recognition":[18],"in":[19,36,98],"images":[20,61],"video":[22,82,109],"is":[23,41,79,110],"to":[24,27,44,67,80],"be":[25,150,163],"able":[26],"generate":[28],"sufficient":[29],"quantities":[30],"labeled":[32,134,142],"training":[33,75,87,198],"image":[34],"data":[35],"a":[37,57,74,84,156,175],"cost-effective":[38],"way.":[39],"It":[40],"generally":[42],"preferred":[43],"tag":[45],"hundreds":[46,69],"thousands":[48],"frames":[50,115,122,140],"each":[52,99],"category":[53],"or":[54],"label,":[55],"human":[58,90,157,176],"being":[59],"tagging":[60],"frame":[62,64,179],"by":[63,155,180,191,193],"might":[65],"expect":[66],"spend":[68],"hours":[71],"creating":[72],"set.":[76],"alternative":[78],"use":[81],"source":[85],"images.":[88],"A":[89],"tagger":[91,177],"notes":[92],"start":[94],"stop":[96],"time":[97,127,153],"clip":[100],"appearance":[103],"objects":[105,130],"interest.":[107],"The":[108,121,152,182],"broken":[111],"down":[112],"into":[113],"component":[114],"software":[117],"ffmpeg.":[120],"that":[123],"fall":[124],"within":[125],"intervals":[128],"interest":[132],"are":[133,141],"\u201ctargets,\u201d":[136],"remaining":[139],"\u201cnon-targets.\u201d":[144],"This":[145],"separation":[146],"categories":[148],"can":[149,190],"automated.":[151],"required":[154],"viewer":[158],"this":[160],"method":[161],"would":[162],"around":[164],"ten":[165],"hours,":[166],"at":[167],"least":[168],"1-2":[169],"orders":[170],"magnitude":[172],"lower":[173],"than":[174],"labeling":[178],"frame.":[181],"false":[183],"alarm":[184],"rate":[185,189],"target":[187],"detection":[188],"optimized":[192],"providing":[194],"system":[196],"unambiguous":[197],"examples.":[199]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}