{"id":"https://openalex.org/W4281734669","doi":"https://doi.org/10.1109/iv51971.2022.9827344","title":"Transformers for Multi-Object Tracking on Point Clouds","display_name":"Transformers for Multi-Object Tracking on Point Clouds","publication_year":2022,"publication_date":"2022-06-05","ids":{"openalex":"https://openalex.org/W4281734669","doi":"https://doi.org/10.1109/iv51971.2022.9827344"},"language":"en","primary_location":{"id":"doi:10.1109/iv51971.2022.9827344","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827344","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2205.15730","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5078398825","display_name":"Felicia Ruppel","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":"Felicia Ruppel","raw_affiliation_strings":["Robert Bosch GmbH, Corporate Research,Renningen,Germany,71272"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH, Corporate Research,Renningen,Germany,71272","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082422947","display_name":"Florian Faion","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":"Florian Faion","raw_affiliation_strings":["Robert Bosch GmbH, Corporate Research,Renningen,Germany,71272"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH, Corporate Research,Renningen,Germany,71272","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046877330","display_name":"Claudius Gl\u00e4ser","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":"Claudius Glaser","raw_affiliation_strings":["Robert Bosch GmbH, Corporate Research,Renningen,Germany,71272"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Robert Bosch GmbH, Corporate Research,Renningen,Germany,71272","institution_ids":["https://openalex.org/I889804353"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085054529","display_name":"Klaus Dietmayer","orcid":"https://orcid.org/0000-0002-1651-014X"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Klaus Dietmayer","raw_affiliation_strings":["Institute of Measurement, Control and Microtechnology, Ulm University,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control and Microtechnology, Ulm University,Germany","institution_ids":["https://openalex.org/I196349391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"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":0.03961099,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"852","last_page":"859"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.22310000658035278,"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":0.22310000658035278,"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":0.16519999504089355,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.1289999932050705,"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":0.7525478601455688},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.6519257426261902},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.610071063041687},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6096283197402954},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6096035838127136},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4804372489452362},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.4780714511871338},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.46476444602012634},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.45713719725608826},{"id":"https://openalex.org/keywords/radar-tracker","display_name":"Radar tracker","score":0.43014830350875854},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42942214012145996},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.32326868176460266},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.2512935400009155},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1478140950202942}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7525478601455688},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6519257426261902},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.610071063041687},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6096283197402954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6096035838127136},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4804372489452362},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.4780714511871338},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.46476444602012634},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.45713719725608826},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.43014830350875854},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42942214012145996},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.32326868176460266},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2512935400009155},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1478140950202942},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"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":3,"locations":[{"id":"doi:10.1109/iv51971.2022.9827344","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv51971.2022.9827344","pdf_url":null,"source":{"id":"https://openalex.org/S4363605370","display_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2205.15730","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.15730","pdf_url":"https://arxiv.org/pdf/2205.15730","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2205.15730","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2205.15730","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2205.15730","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2205.15730","pdf_url":"https://arxiv.org/pdf/2205.15730","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W2082179954","https://openalex.org/W2920942303","https://openalex.org/W2966535964","https://openalex.org/W2968296999","https://openalex.org/W2970095196","https://openalex.org/W2983227562","https://openalex.org/W3034679090","https://openalex.org/W3034782805","https://openalex.org/W3035574168","https://openalex.org/W3095753995","https://openalex.org/W3132607695","https://openalex.org/W3143320354","https://openalex.org/W3190141195","https://openalex.org/W3205586691","https://openalex.org/W3207707490","https://openalex.org/W4214624153","https://openalex.org/W6681302627","https://openalex.org/W6739778489","https://openalex.org/W6739901393","https://openalex.org/W6772381950","https://openalex.org/W6778485988","https://openalex.org/W6779163297","https://openalex.org/W6780179280","https://openalex.org/W6783267081","https://openalex.org/W6784333009","https://openalex.org/W6787985011","https://openalex.org/W6810276735"],"related_works":["https://openalex.org/W9513853","https://openalex.org/W9122165","https://openalex.org/W4017687","https://openalex.org/W6467838","https://openalex.org/W669531","https://openalex.org/W1456570","https://openalex.org/W1424933","https://openalex.org/W12174787","https://openalex.org/W11776655","https://openalex.org/W7746136"],"abstract_inverted_index":{"We":[0],"present":[1],"TransMOT,":[2],"a":[3,19,22,130,149,157],"novel":[4,131],"transformer-based":[5],"end-to-end":[6],"trainable":[7],"online":[8],"tracker":[9,63],"and":[10,21,25,47,61,146],"detector":[11,83],"for":[12,84],"point":[13],"cloud":[14],"data.":[15],"The":[16],"model":[17,172],"utilizes":[18],"cross-":[20],"self-attention":[23],"mechanism":[24],"is":[26,65,126],"applicable":[27],"to":[28,97,113,121,129,141],"lidar":[29],"data":[30,39],"in":[31,67],"an":[32,111],"automotive":[33],"context,":[34],"as":[35,37,42,110,152],"well":[36],"other":[38],"types,":[40],"such":[41,109],"radar.":[43],"Both":[44],"track":[45,136],"management":[46],"the":[48,56,62,77,82,101,135,142,153,164,170],"detection":[49],"of":[50,76,81,100,104,156],"new":[51],"tracks":[52],"are":[53,95,161],"performed":[54],"by":[55],"same":[57],"transformer":[58],"decoder":[59],"module":[60,132],"state":[64],"encoded":[66],"feature":[68],"space.":[69],"With":[70],"this":[71],"approach,":[72],"we":[73,94],"make":[74],"use":[75],"rich":[78],"latent":[79],"space":[80],"tracking":[85,177],"rather":[86],"than":[87],"relying":[88],"on":[89,144,163],"low-dimensional":[90],"bounding":[91],"boxes.":[92],"Still,":[93],"able":[96],"retain":[98],"some":[99],"desirable":[102],"properties":[103],"traditional":[105],"Kalman-filter":[106],"based":[107],"approaches,":[108],"ability":[112],"handle":[114],"sensor":[115],"input":[116],"at":[117],"arbitrary":[118],"timesteps":[119],"or":[120],"compensate":[122],"frame":[123,140],"skips.":[124],"This":[125],"possible":[127],"due":[128],"that":[133],"transforms":[134],"information":[137],"from":[138],"one":[139],"next":[143],"feature-level":[145],"thereby":[147],"fulfills":[148],"similar":[150],"task":[151],"prediction":[154],"step":[155],"Kalman":[158,175],"filter.":[159],"Results":[160],"presented":[162],"challenging":[165],"real-world":[166],"dataset":[167],"nuScenes,":[168],"where":[169],"proposed":[171],"outperforms":[173],"its":[174],"filter-based":[176],"baseline.":[178]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2022-06-13T00:00:00"}