{"id":"https://openalex.org/W4416961931","doi":"https://doi.org/10.1109/ist66504.2025.11268431","title":"3D Spiking Convolutional Networks for Robust Human Activity Recognition in AV Cabins","display_name":"3D Spiking Convolutional Networks for Robust Human Activity Recognition in AV Cabins","publication_year":2025,"publication_date":"2025-10-15","ids":{"openalex":"https://openalex.org/W4416961931","doi":"https://doi.org/10.1109/ist66504.2025.11268431"},"language":null,"primary_location":{"id":"doi:10.1109/ist66504.2025.11268431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","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/A5017064039","display_name":"D.E. Manolakis","orcid":null},"institutions":[{"id":"https://openalex.org/I4210093649","display_name":"Information Technologies Institute","ror":"https://ror.org/0069akp70","country_code":"GR","type":"nonprofit","lineage":["https://openalex.org/I4210093649"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Dimitris Manolakis","raw_affiliation_strings":["Information Technologies Institute,Centre for Research and Technology,Hellas,Greece"],"affiliations":[{"raw_affiliation_string":"Information Technologies Institute,Centre for Research and Technology,Hellas,Greece","institution_ids":["https://openalex.org/I4210093649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051582957","display_name":"Antonios Lalas","orcid":"https://orcid.org/0000-0002-5337-161X"},"institutions":[{"id":"https://openalex.org/I4210093649","display_name":"Information Technologies Institute","ror":"https://ror.org/0069akp70","country_code":"GR","type":"nonprofit","lineage":["https://openalex.org/I4210093649"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Antonios Lalas","raw_affiliation_strings":["Information Technologies Institute,Centre for Research and Technology,Hellas,Greece"],"affiliations":[{"raw_affiliation_string":"Information Technologies Institute,Centre for Research and Technology,Hellas,Greece","institution_ids":["https://openalex.org/I4210093649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041627686","display_name":"Katerina Maria Oikonomou","orcid":"https://orcid.org/0000-0001-7537-7310"},"institutions":[{"id":"https://openalex.org/I4210093649","display_name":"Information Technologies Institute","ror":"https://ror.org/0069akp70","country_code":"GR","type":"nonprofit","lineage":["https://openalex.org/I4210093649"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Katerina Maria Oikonomou","raw_affiliation_strings":["Information Technologies Institute,Centre for Research and Technology,Hellas,Greece"],"affiliations":[{"raw_affiliation_string":"Information Technologies Institute,Centre for Research and Technology,Hellas,Greece","institution_ids":["https://openalex.org/I4210093649"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084139328","display_name":"\u0391\u03bd\u03c4\u03ce\u03bd\u03b9\u03bf\u03c2 \u0393\u03b1\u03c3\u03c4\u03b5\u03c1\u03ac\u03c4\u03bf\u03c2","orcid":"https://orcid.org/0000-0002-5421-0332"},"institutions":[{"id":"https://openalex.org/I147962203","display_name":"Democritus University of Thrace","ror":"https://ror.org/03bfqnx40","country_code":"GR","type":"education","lineage":["https://openalex.org/I147962203"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Antonios Gasteratos","raw_affiliation_strings":["Democritus University of Thrace,Department of Production and Management Engineering,Greece"],"affiliations":[{"raw_affiliation_string":"Democritus University of Thrace,Department of Production and Management Engineering,Greece","institution_ids":["https://openalex.org/I147962203"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064307246","display_name":"Konstantinos Votis","orcid":"https://orcid.org/0000-0001-6381-8326"},"institutions":[{"id":"https://openalex.org/I4210093649","display_name":"Information Technologies Institute","ror":"https://ror.org/0069akp70","country_code":"GR","type":"nonprofit","lineage":["https://openalex.org/I4210093649"]}],"countries":["GR"],"is_corresponding":false,"raw_author_name":"Konstantinos Votis","raw_affiliation_strings":["Information Technologies Institute,Centre for Research and Technology,Hellas,Greece"],"affiliations":[{"raw_affiliation_string":"Information Technologies Institute,Centre for Research and Technology,Hellas,Greece","institution_ids":["https://openalex.org/I4210093649"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5017064039"],"corresponding_institution_ids":["https://openalex.org/I4210093649"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41746106,"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":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.3871000111103058,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.3871000111103058,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.17399999499320984,"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/T10429","display_name":"EEG and Brain-Computer Interfaces","score":0.06340000033378601,"subfield":{"id":"https://openalex.org/subfields/2805","display_name":"Cognitive Neuroscience"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6462000012397766},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6218000054359436},{"id":"https://openalex.org/keywords/spiking-neural-network","display_name":"Spiking neural network","score":0.6079999804496765},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6022999882698059},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.5005000233650208},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.4733000099658966},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42730000615119934},{"id":"https://openalex.org/keywords/spike","display_name":"Spike (software development)","score":0.41990000009536743}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7756999731063843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6717000007629395},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6462000012397766},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6218000054359436},{"id":"https://openalex.org/C11731999","wikidata":"https://www.wikidata.org/wiki/Q9067355","display_name":"Spiking neural network","level":3,"score":0.6079999804496765},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6022999882698059},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.5005000233650208},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42730000615119934},{"id":"https://openalex.org/C2781390188","wikidata":"https://www.wikidata.org/wiki/Q25203449","display_name":"Spike (software development)","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C2909946758","wikidata":"https://www.wikidata.org/wiki/Q194277","display_name":"Spike train","level":3,"score":0.40529999136924744},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4020000100135803},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.335999995470047},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.313400000333786},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28119999170303345},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.2678999900817871},{"id":"https://openalex.org/C159919123","wikidata":"https://www.wikidata.org/wiki/Q7577157","display_name":"Spike-timing-dependent plasticity","level":4,"score":0.26669999957084656},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2632000148296356}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist66504.2025.11268431","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268431","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1983364832","https://openalex.org/W2469278928","https://openalex.org/W2507540959","https://openalex.org/W2796368857","https://openalex.org/W2904275768","https://openalex.org/W2950380208","https://openalex.org/W2963510238","https://openalex.org/W2964273528","https://openalex.org/W3169332058","https://openalex.org/W3189897383","https://openalex.org/W4362654522","https://openalex.org/W4375850379","https://openalex.org/W4396926107","https://openalex.org/W4399729040","https://openalex.org/W4400410123","https://openalex.org/W4403887598","https://openalex.org/W4404624582","https://openalex.org/W4406564051","https://openalex.org/W4406704163"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,33,152,158,177],"novel":[4],"3D":[5,58],"Spiking":[6],"Convolutional":[7],"Neural":[8],"Network":[9],"(3D-SCNN)":[10],"designed":[11],"for":[12,198],"Human":[13],"Action":[14],"Recognition":[15],"(HAR)":[16],"in":[17,203],"autonomous":[18,46,204],"vehicles,":[19],"leveraging":[20],"both":[21,109],"Red,":[22],"Green":[23],"and":[24,27,41,77,111,122,133,157,196],"Blue":[25],"(RGB)":[26],"event-based":[28,42,97,188],"visual":[29],"modalities.":[30],"We":[31],"introduce":[32],"dual-modality":[34],"HAR":[35,146,201],"dataset":[36],"composed":[37],"of":[38,80,118,155,162],"synchronized":[39],"RGB":[40,85,173],"recordings":[43],"captured":[44],"inside":[45],"minibus":[47],"cabins":[48],"under":[49],"realistic":[50],"driving":[51],"conditions.":[52],"The":[53,138],"proposed":[54],"3D-SCNN":[55],"architecture":[56],"combines":[57],"convolutions":[59],"with":[60],"Leaky":[61],"Integrate-and-Fire":[62],"(LIF)":[63],"neurons,":[64],"enabling":[65],"the":[66,75,116,144,169,193],"network":[67],"to":[68,126,181],"capture":[69],"spatio-temporal":[70],"patterns":[71],"while":[72,96],"benefiting":[73],"from":[74],"efficiency":[76],"biological":[78],"plausibility":[79],"spiking":[81],"neural":[82],"networks":[83],"(SNNs).":[84],"frames":[86],"are":[87,99],"encoded":[88],"into":[89],"spike":[90],"trains":[91],"using":[92],"random":[93],"temporal":[94,106],"coding,":[95],"data":[98,174],"represented":[100],"as":[101],"event":[102],"cubes,":[103],"preserving":[104],"fine-grained":[105],"structure":[107],"across":[108],"time":[110],"space.":[112],"Extensive":[113],"experiments":[114],"explore":[115],"impact":[117],"different":[119],"architectural":[120],"configurations":[121],"loss":[123],"functions":[124],"tailored":[125],"SNNs,":[127],"including":[128],"Spike":[129,131],"Count,":[130],"Rate,":[132],"Maximum":[134],"Membrane":[135],"potential":[136],"loss.":[137],"model":[139,170],"achieves":[140],"strong":[141],"results":[142,191],"on":[143,171,186],"eventbased":[145],"task,":[147],"reaching":[148],"$81":[149],"\\%$":[150],"accuracy,":[151],"macro":[153],"F1-score":[154],"0.82,":[156],"weighted":[159],"$F":[160],"1$-score":[161],"0.80.":[163],"Crucially,":[164],"we":[165],"find":[166],"that":[167],"pre-training":[168],"spike-encoded":[172],"can":[175],"provide":[176],"beneficial":[178],"initialization,":[179],"leading":[180],"improved":[182],"performance":[183],"when":[184],"fine-tuned":[185],"real":[187],"data.":[189],"These":[190],"highlight":[192],"model\u2019s":[194],"robustness":[195],"suitability":[197],"low-power,":[199],"real-time":[200],"applications":[202],"mobility":[205],"settings.":[206]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-12-03T00:00:00"}
