{"id":"https://openalex.org/W2990893531","doi":"https://doi.org/10.1109/itsc.2019.8917160","title":"Generating 3D Person Trajectories from Sparse Image Annotations in an Intelligent Vehicles Setting","display_name":"Generating 3D Person Trajectories from Sparse Image Annotations in an Intelligent Vehicles Setting","publication_year":2019,"publication_date":"2019-10-01","ids":{"openalex":"https://openalex.org/W2990893531","doi":"https://doi.org/10.1109/itsc.2019.8917160","mag":"2990893531"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2019.8917160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917160","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","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/A5002815046","display_name":"Sebastian Krebs","orcid":"https://orcid.org/0000-0002-2740-6719"},"institutions":[{"id":"https://openalex.org/I891521709","display_name":"Daimler (Germany)","ror":"https://ror.org/00m0j3d84","country_code":"DE","type":"company","lineage":["https://openalex.org/I891521709"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Sebastian Krebs","raw_affiliation_strings":["Environment Perception, Daimler AG, Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Environment Perception, Daimler AG, Stuttgart, Germany","institution_ids":["https://openalex.org/I891521709"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5001459580","display_name":"Markus Braun","orcid":"https://orcid.org/0000-0003-1439-850X"},"institutions":[{"id":"https://openalex.org/I891521709","display_name":"Daimler (Germany)","ror":"https://ror.org/00m0j3d84","country_code":"DE","type":"company","lineage":["https://openalex.org/I891521709"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Markus Braun","raw_affiliation_strings":["Environment Perception, Daimler AG, Stuttgart, Germany"],"affiliations":[{"raw_affiliation_string":"Environment Perception, Daimler AG, Stuttgart, Germany","institution_ids":["https://openalex.org/I891521709"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085298812","display_name":"Dariu M. Gavrila","orcid":"https://orcid.org/0000-0002-1810-4196"},"institutions":[{"id":"https://openalex.org/I98358874","display_name":"Delft University of Technology","ror":"https://ror.org/02e2c7k09","country_code":"NL","type":"education","lineage":["https://openalex.org/I98358874"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Dariu M. Gavrila","raw_affiliation_strings":["Intelligent Vehicles, Technical University, Delft, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Intelligent Vehicles, Technical University, Delft, The Netherlands","institution_ids":["https://openalex.org/I98358874"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5002815046"],"corresponding_institution_ids":["https://openalex.org/I891521709"],"apc_list":null,"apc_paid":null,"fwci":0.1012,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.45904415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"783","last_page":"788"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9994000196456909,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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":0.7427104711532593},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.724719762802124},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6534392833709717},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.6336889863014221},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.5160576701164246},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4953829050064087},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4475986957550049},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4397713243961334},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4303935170173645},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.415314257144928},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33204659819602966}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7427104711532593},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.724719762802124},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6534392833709717},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.6336889863014221},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.5160576701164246},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4953829050064087},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4475986957550049},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4397713243961334},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4303935170173645},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.415314257144928},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33204659819602966},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/itsc.2019.8917160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2019.8917160","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:tudelft.nl:uuid:f7c6c4f8-470b-4963-8bcf-f302e95e96b7","is_oa":false,"landing_page_url":"http://resolver.tudelft.nl/uuid:f7c6c4f8-470b-4963-8bcf-f302e95e96b7","pdf_url":null,"source":{"id":"https://openalex.org/S4306400906","display_name":"Research Repository (Delft University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I98358874","host_organization_name":"Delft University of Technology","host_organization_lineage":["https://openalex.org/I98358874"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"conference paper"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1514923623","https://openalex.org/W1521019969","https://openalex.org/W1686810756","https://openalex.org/W2016135469","https://openalex.org/W2031454541","https://openalex.org/W2083049794","https://openalex.org/W2104828970","https://openalex.org/W2107775979","https://openalex.org/W2111644456","https://openalex.org/W2117907414","https://openalex.org/W2145938889","https://openalex.org/W2154434560","https://openalex.org/W2163385949","https://openalex.org/W2163605009","https://openalex.org/W2171243491","https://openalex.org/W2279083934","https://openalex.org/W2316292243","https://openalex.org/W2520433280","https://openalex.org/W2546643512","https://openalex.org/W2596030096","https://openalex.org/W2613718673","https://openalex.org/W2803740064","https://openalex.org/W4295331127","https://openalex.org/W6620707391","https://openalex.org/W6631061280","https://openalex.org/W6637373629","https://openalex.org/W6676780830","https://openalex.org/W6677657710","https://openalex.org/W6681158678","https://openalex.org/W6684191040","https://openalex.org/W6726951882"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W2183246718","https://openalex.org/W2099261052","https://openalex.org/W3209204065","https://openalex.org/W2366906938","https://openalex.org/W2105707930","https://openalex.org/W1755711892","https://openalex.org/W2160907113","https://openalex.org/W2070813941","https://openalex.org/W3166204570"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3,30],"approach":[4,20,89],"to":[5,81,93],"generate":[6],"dense":[7],"person":[8,60,79,102],"3D":[9,41,97],"trajectories":[10],"from":[11,43],"sparse":[12,57],"image":[13,46],"annotations":[14,61],"on-board":[15],"a":[16,76],"moving":[17],"platform.":[18],"Our":[19],"leverages":[21],"the":[22,73,95,117],"additional":[23],"information":[24],"that":[25,62],"is":[26,90],"typically":[27],"available":[28,64],"in":[29],"intelligent":[31],"vehicle":[32],"setting,":[33],"such":[34],"as":[35],"LiDAR":[36],"sensor":[37],"measurements":[38],"(to":[39,52],"obtain":[40,82],"positions":[42],"detected":[44],"2D":[45,59,78],"bounding":[47],"boxes)":[48],"and":[49,111],"inertial":[50],"sensing":[51],"perform":[53],"ego-motion":[54],"compensation).":[55],"The":[56],"manual":[58],"are":[63,70],"at":[65],"regular":[66],"time":[67],"intervals":[68],"(key-frames)":[69],"augmented":[71],"with":[72],"output":[74,104],"of":[75],"state-of-the-art":[77],"detector,":[80],"frame-wise":[83],"data.":[84],"A":[85],"graph-based":[86],"batch":[87],"optimization":[88],"subsequently":[91],"performed":[92],"find":[94],"best":[96],"trajectories,":[98],"accounting":[99],"for":[100],"erroneous":[101],"detector":[103],"(false":[105],"positives,":[106],"false":[107],"negatives,":[108],"imprecise":[109],"localization)":[110],"unknown":[112],"temporal":[113],"correspondences.":[114],"Experiments":[115],"on":[116],"EuroCity":[118],"Persons":[119],"dataset":[120],"show":[121],"promising":[122],"results.":[123]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
