{"id":"https://openalex.org/W2983493483","doi":"https://doi.org/10.2312/egve.20191273","title":"Random-Forest-Based Initializer for Real-time Optimization-based 3D Motion Tracking Problems","display_name":"Random-Forest-Based Initializer for Real-time Optimization-based 3D Motion Tracking Problems","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2983493483","doi":"https://doi.org/10.2312/egve.20191273","mag":"2983493483"},"language":"en","primary_location":{"id":"doi:10.2312/egve.20191273","is_oa":true,"landing_page_url":"https://doi.org/10.2312/egve.20191273","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.2312/egve.20191273","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100601282","display_name":"Jiawei Huang","orcid":"https://orcid.org/0000-0001-7670-2971"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jiawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014830158","display_name":"Ryo Sugawara","orcid":"https://orcid.org/0000-0003-3824-6682"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sugawara, Ryo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014324841","display_name":"Taku Komura","orcid":"https://orcid.org/0000-0002-2729-5860"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Komura, Taku","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5026275950","display_name":"Yoshifumi Kitamura","orcid":"https://orcid.org/0000-0002-7047-627X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kitamura, Yoshifumi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12376044,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9886999726295471,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.9886999726295471,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9851999878883362,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10586","display_name":"Robotic Path Planning Algorithms","score":0.9769999980926514,"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/random-forest","display_name":"Random forest","score":0.5742917060852051},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5532700419425964},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5150325298309326},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4922238886356354},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4493086338043213},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.42308902740478516}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5742917060852051},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5532700419425964},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5150325298309326},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4922238886356354},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4493086338043213},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.42308902740478516},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.2312/egve.20191273","is_oa":true,"landing_page_url":"https://doi.org/10.2312/egve.20191273","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article-journal"},{"id":"mag:2983493483","is_oa":false,"landing_page_url":"https://diglib.eg.org/handle/10.2312/egve20191273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":null}],"best_oa_location":{"id":"doi:10.2312/egve.20191273","is_oa":true,"landing_page_url":"https://doi.org/10.2312/egve.20191273","pdf_url":null,"source":{"id":"https://openalex.org/S7407052899","display_name":"Eurographics","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/13","display_name":"Climate action","score":0.44999998807907104}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1966312294","https://openalex.org/W2096274779","https://openalex.org/W2012949382","https://openalex.org/W2167460480","https://openalex.org/W2121758370","https://openalex.org/W2123073649","https://openalex.org/W2265554555","https://openalex.org/W2164626806","https://openalex.org/W2410641873","https://openalex.org/W2952005228","https://openalex.org/W2602219729","https://openalex.org/W1609306619","https://openalex.org/W1587687651","https://openalex.org/W2094665513","https://openalex.org/W2135238428","https://openalex.org/W1583796080","https://openalex.org/W1528633625","https://openalex.org/W181030707","https://openalex.org/W2936194595","https://openalex.org/W1591169274"],"abstract_inverted_index":{"Many":[0],"motion":[1,28,158],"tracking":[2,11,73,78,159,245,252],"systems":[3],"require":[4],"solving":[5,38,199],"inverse":[6,40,118,201],"problem":[7,119,202],"to":[8,46,58,98,110,150,213],"compute":[9],"the":[10,34,48,180,187,193,200,219,255],"result":[12],"from":[13,20,24,179,244],"original":[14],"sensor":[15,184],"measurements,":[16,185],"such":[17,30],"as":[18,33,137],"images":[19],"cameras":[21],"and":[22,76,94,131,134,186,196,216,254,259],"signals":[23],"receivers.":[25],"For":[26],"real-time":[27,117,155],"tracking,":[29],"typical":[31],"solutions":[32],"Gauss-Newton":[35],"method":[36,122,241],"for":[37,64,69,115,140],"their":[39],"problems":[41],"need":[42],"an":[43,138,144,204],"initial":[44,62,88,113,175],"value":[45,63,89,176,231],"optimize":[47],"cost":[49],"function":[50],"through":[51,203],"iterations.":[52],"A":[53],"powerful":[54],"initializer":[55,106,139,149,181],"is":[56,83,153,177],"crucial":[57],"generate":[59],"a":[60,104,124,154,173,229,248],"proper":[61,112,174],"every":[65],"time":[66],"instance":[67],"and,":[68],"achieving":[70],"continuous":[71],"accurate":[72],"without":[74],"errors":[75],"rapid":[77],"recovery":[79,243],"even":[80],"when":[81],"it":[82,136],"temporally":[84],"interrupted.":[85],"An":[86],"improper":[87],"easily":[90],"causes":[91],"optimization":[92,205],"divergence,":[93],"cannot":[95],"always":[96],"lead":[97],"reasonable":[99],"solutions.":[100],"Therefore,":[101],"we":[102,146],"propose":[103],"new":[105],"based":[107,182],"on":[108,183],"random-forest":[109,125],"obtain":[111],"values":[114],"efficient":[116],"computation.":[120],"Our":[121],"trains":[123],"model":[126],"with":[127,161,222],"varied":[128],"massive":[129],"inputs":[130],"corresponding":[132],"outputs":[133],"uses":[135],"runtime":[141],"optimization.":[142],"As":[143],"instance,":[145],"apply":[147],"our":[148,235,239],"IM3D,":[151],"which":[152],"magnetic":[156],"3D":[157],"system":[160,188],"multiple":[162],"tiny,":[163],"identifiable,":[164],"wireless,":[165],"occlusion-free":[166],"passive":[167],"markers":[168,195],"(LC":[169],"coils).":[170],"During":[171],"run-time,":[172],"obtained":[178],"computes":[189],"each":[190],"position":[191],"of":[192,218,251],"actual":[194],"poses":[197],"by":[198],"process":[206,257],"in":[207,247,263],"real-time.":[208,264],"We":[209],"conduct":[210],"four":[211],"experiments":[212],"evaluate":[214],"reliability":[215],"performance":[217],"initializer.":[220],"Compared":[221],"traditional":[223],"or":[224,232],"naive":[225],"initializers":[226],"(i.e.,":[227],"using":[228],"static":[230],"random":[233],"values),":[234],"results":[236],"show":[237],"that":[238],"proposed":[240],"provides":[242],"loss":[246],"wider":[249],"range":[250],"space,":[253],"entire":[256],"(initialization":[258],"optimization)":[260],"can":[261],"run":[262]},"counts_by_year":[],"updated_date":"2025-11-06T06:51:31.235846","created_date":"2025-10-10T00:00:00"}
