{"id":"https://openalex.org/W7080116673","doi":"https://doi.org/10.48550/arxiv.2509.04117","title":"DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset","display_name":"DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset","publication_year":2025,"publication_date":"2025-09-04","ids":{"openalex":"https://openalex.org/W7080116673","doi":"https://doi.org/10.48550/arxiv.2509.04117"},"language":"en","primary_location":{"id":"doi:10.48550/arxiv.2509.04117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.04117","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2509.04117","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Sakhai, Mustafa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sakhai, Mustafa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Sithu, Kaung","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sithu, Kaung","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Oke, Min Khant Soe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oke, Min Khant Soe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Wielgosz, Maciej","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wielgosz, Maciej","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6158999800682068,"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"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.6158999800682068,"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"}},{"id":"https://openalex.org/T13067","display_name":"Geological Modeling and Analysis","score":0.03440000116825104,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14311","display_name":"Electrical and Electromagnetic Research","score":0.019300000742077827,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/usability","display_name":"Usability","score":0.6978999972343445},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6859999895095825},{"id":"https://openalex.org/keywords/neuromorphic-engineering","display_name":"Neuromorphic engineering","score":0.527899980545044},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.49239999055862427},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4851999878883362},{"id":"https://openalex.org/keywords/metadata","display_name":"Metadata","score":0.4832000136375427},{"id":"https://openalex.org/keywords/event-monitoring","display_name":"Event monitoring","score":0.44179999828338623},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.3828999996185303}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7547000050544739},{"id":"https://openalex.org/C170130773","wikidata":"https://www.wikidata.org/wiki/Q216378","display_name":"Usability","level":2,"score":0.6978999972343445},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6859999895095825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5630999803543091},{"id":"https://openalex.org/C151927369","wikidata":"https://www.wikidata.org/wiki/Q1981312","display_name":"Neuromorphic engineering","level":3,"score":0.527899980545044},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5228999853134155},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.49239999055862427},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4851999878883362},{"id":"https://openalex.org/C93518851","wikidata":"https://www.wikidata.org/wiki/Q180160","display_name":"Metadata","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C2778135862","wikidata":"https://www.wikidata.org/wiki/Q5416719","display_name":"Event monitoring","level":3,"score":0.44179999828338623},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3828999996185303},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.34950000047683716},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.329800009727478},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C2780624872","wikidata":"https://www.wikidata.org/wiki/Q852453","display_name":"Motion detection","level":3,"score":0.27399998903274536},{"id":"https://openalex.org/C2777036941","wikidata":"https://www.wikidata.org/wiki/Q6917771","display_name":"Motion analysis","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2509.04117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.04117","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2509.04117","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.04117","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6118416786193848}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Event":[0],"cameras":[1],"like":[2],"Dynamic":[3],"Vision":[4,26],"Sensors":[5],"(DVS)":[6],"report":[7],"micro-timed":[8],"brightness":[9],"changes":[10],"instead":[11],"of":[12],"full":[13],"frames,":[14,93],"offering":[15],"low":[16],"latency,":[17],"high":[18],"dynamic":[19],"range,":[20],"and":[21,38,43,67,69,86,101,117,122,137,145,159],"motion":[22],"robustness.":[23],"DVS-PedX":[24,148],"(Dynamic":[25],"Sensor":[27],"Pedestrian":[28],"eXploration)":[29],"is":[30],"a":[31,139],"neuromorphic":[32,160],"dataset":[33,135],"designed":[34],"for":[35,60,124],"pedestrian":[36,155],"detection":[37],"crossing-intention":[39],"analysis":[40],"in":[41,56,153],"normal":[42],"adverse":[44],"weather":[45,66],"conditions":[46],"across":[47],"two":[48],"complementary":[49],"sources:":[50],"(1)":[51],"synthetic":[52],"event":[53,77,115],"streams":[54,78],"generated":[55],"the":[57,80],"CARLA":[58],"simulator":[59],"controlled":[61],"\"approach-cross\"":[62],"scenes":[63],"under":[64],"varied":[65],"lighting;":[68],"(2)":[70],"real-world":[71],"JAAD":[72],"dash-cam":[73],"videos":[74],"converted":[75],"to":[76,150],"using":[79,132],"v2e":[81],"tool,":[82],"preserving":[83],"natural":[84],"behaviors":[85],"backgrounds.":[87],"Each":[88],"sequence":[89],"includes":[90],"paired":[91],"RGB":[92],"per-frame":[94],"DVS":[95,119],"\"event":[96],"frames\"":[97],"(33":[98],"ms":[99],"accumulations),":[100],"frame-level":[102],"labels":[103],"(crossing":[104],"vs.":[105],"not":[106],"crossing).":[107],"We":[108],"also":[109],"provide":[110],"raw":[111],"AEDAT":[112],"2.0/AEDAT":[113],"4.0":[114],"files":[116,121],"AVI":[118],"video":[120],"metadata":[123],"flexible":[125],"re-processing.":[126],"Baseline":[127],"spiking":[128],"neural":[129],"networks":[130],"(SNNs)":[131],"SpikingJelly":[133],"illustrate":[134],"usability":[136],"reveal":[138],"sim-to-real":[140],"gap,":[141],"motivating":[142],"domain":[143],"adaptation":[144],"multimodal":[146],"fusion.":[147],"aims":[149],"accelerate":[151],"research":[152],"event-based":[154],"safety,":[156],"intention":[157],"prediction,":[158],"perception.":[161]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}