{"id":"https://openalex.org/W7165741879","doi":"https://doi.org/10.48550/arxiv.2606.24499","title":"GeoIMO: Geometry-Driven Independent Motion Classification for Event Cameras","display_name":"GeoIMO: Geometry-Driven Independent Motion Classification for Event Cameras","publication_year":2026,"publication_date":"2026-06-23","ids":{"openalex":"https://openalex.org/W7165741879","doi":"https://doi.org/10.48550/arxiv.2606.24499"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.24499","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24499","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.24499","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5094349192","display_name":"An\u0131l Bayram G\u00f6\u011febakan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gogebakan, Anil Bayram","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5119450544","display_name":"Filippo Marostica","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marostica, Filippo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136198130","display_name":"Alessio Caviglia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Caviglia, Alessio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065582630","display_name":"Alessandro Savino","orcid":"https://orcid.org/0000-0003-0529-7950"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Savino, Alessandro","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139296413","display_name":"Stefano Di Carlo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Di Carlo, Stefano","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":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.8338000178337097,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.8338000178337097,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.03750000149011612,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.014499999582767487,"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/bounding-overwatch","display_name":"Bounding overwatch","score":0.7027000188827515},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6769000291824341},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5293999910354614},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.49410000443458557},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.48240000009536743},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.41370001435279846},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.40720000863075256},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.34310001134872437}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7213000059127808},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7099000215530396},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.7027000188827515},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7013000249862671},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6769000291824341},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5293999910354614},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.49410000443458557},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.48240000009536743},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.41370001435279846},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.40720000863075256},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.34310001134872437},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C128840427","wikidata":"https://www.wikidata.org/wiki/Q1302174","display_name":"Motion compensation","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C2777036941","wikidata":"https://www.wikidata.org/wiki/Q6917771","display_name":"Motion analysis","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C2987896495","wikidata":"https://www.wikidata.org/wiki/Q5416716","display_name":"Event data","level":3,"score":0.31049999594688416},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.3009999990463257},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.26080000400543213}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.24499","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24499","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.24499","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.24499","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.4229453206062317,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"automotive":[1],"event":[2,17,41,80],"datasets":[3],"rely":[4],"on":[5,99],"appearance-based":[6],"annotations":[7],"from":[8,39,65],"frame":[9],"pipelines,":[10],"making":[11],"them":[12],"poorly":[13],"suited":[14],"for":[15],"motion-aware":[16],"perception.":[18],"We":[19],"present":[20],"a":[21,71,124,130],"geometry-driven,":[22],"annotation-free":[23],"framework":[24],"that":[25],"classifies":[26],"detected":[27],"objects":[28,56],"as":[29,59,68],"static":[30],"or":[31],"independently":[32],"moving":[33,60],"by":[34,70],"exploiting":[35],"ego-motion":[36],"structure":[37],"directly":[38],"the":[40,102],"stream.":[42],"A":[43],"Focus":[44],"of":[45],"Expansion":[46],"model":[47,128],"with":[48,93,116],"yaw":[49,117],"compensation":[50,118],"estimates":[51],"global":[52],"background":[53],"motion,":[54],"while":[55],"are":[57],"labeled":[58],"when":[61],"local":[62,127],"motion":[63,89],"deviates":[64],"this":[66],"prediction,":[67],"quantified":[69],"scale-invariant":[72],"residual.":[73],"Temporal":[74],"stabilization":[75],"improves":[76],"robustness":[77],"across":[78,112],"consecutive":[79],"windows.":[81],"The":[82],"method":[83],"requires":[84],"no":[85,87],"learning,":[86],"manual":[88],"labels,":[90],"and":[91,101,123],"works":[92],"any":[94],"input":[95],"bounding":[96],"boxes.":[97],"Experiments":[98],"MVSEC":[100],"Prophesee":[103],"1":[104],"Megapixel":[105],"Automotive":[106],"Detection":[107],"dataset":[108],"demonstrate":[109],"consistent":[110],"performance":[111],"diverse":[113],"driving":[114],"scenarios,":[115],"improving":[119],"results":[120],"during":[121],"turns":[122],"simple":[125],"translational":[126],"offering":[129],"favorable":[131],"accuracy-efficiency":[132],"trade-off.":[133]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-25T00:00:00"}