{"id":"https://openalex.org/W2899960484","doi":"https://doi.org/10.1109/icves.2018.8519497","title":"CNN-LIDAR pedestrian classification: combining range and reflectance data","display_name":"CNN-LIDAR pedestrian classification: combining range and reflectance data","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2899960484","doi":"https://doi.org/10.1109/icves.2018.8519497","mag":"2899960484"},"language":"en","primary_location":{"id":"doi:10.1109/icves.2018.8519497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icves.2018.8519497","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","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/A5084838708","display_name":"Gledson Melotti","orcid":"https://orcid.org/0000-0002-8988-0205"},"institutions":[{"id":"https://openalex.org/I4210125590","display_name":"Institute for Systems Engineering and Computers","ror":"https://ror.org/033wn8m60","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210125590"]},{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":true,"raw_author_name":"Gledson Melotti","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Portugal"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Portugal","institution_ids":["https://openalex.org/I4210125590","https://openalex.org/I76903346"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013510977","display_name":"Alireza Asvadi","orcid":"https://orcid.org/0000-0002-9645-5346"},"institutions":[{"id":"https://openalex.org/I4210125590","display_name":"Institute for Systems Engineering and Computers","ror":"https://ror.org/033wn8m60","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210125590"]},{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Alireza Asvadi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Portugal"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Portugal","institution_ids":["https://openalex.org/I4210125590","https://openalex.org/I76903346"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039508418","display_name":"Cristiano Premebida","orcid":"https://orcid.org/0000-0002-2168-2077"},"institutions":[{"id":"https://openalex.org/I76903346","display_name":"University of Coimbra","ror":"https://ror.org/04z8k9a98","country_code":"PT","type":"education","lineage":["https://openalex.org/I76903346"]},{"id":"https://openalex.org/I4210125590","display_name":"Institute for Systems Engineering and Computers","ror":"https://ror.org/033wn8m60","country_code":"PT","type":"nonprofit","lineage":["https://openalex.org/I4210125590"]}],"countries":["PT"],"is_corresponding":false,"raw_author_name":"Cristiano Premebida","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Portugal"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Institute of Systems and Robotics, University of Coimbra, Portugal","institution_ids":["https://openalex.org/I4210125590","https://openalex.org/I76903346"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5084838708"],"corresponding_institution_ids":["https://openalex.org/I4210125590","https://openalex.org/I76903346"],"apc_list":null,"apc_paid":null,"fwci":0.6268,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.74667154,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9958999752998352,"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/lidar","display_name":"Lidar","score":0.8704346418380737},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7600541114807129},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6987435817718506},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6956405639648438},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.6562879085540771},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6124445796012878},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.5815411806106567},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5190728306770325},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.49274972081184387},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.46557778120040894},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4655269980430603},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.4518170654773712},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4223308265209198},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.23117965459823608},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.12512096762657166},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11752530932426453},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09013286232948303}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8704346418380737},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7600541114807129},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6987435817718506},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6956405639648438},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.6562879085540771},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6124445796012878},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.5815411806106567},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5190728306770325},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.49274972081184387},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.46557778120040894},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4655269980430603},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.4518170654773712},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4223308265209198},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.23117965459823608},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.12512096762657166},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11752530932426453},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09013286232948303},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icves.2018.8519497","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icves.2018.8519497","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES)","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":23,"referenced_works":["https://openalex.org/W1642501641","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W2006488212","https://openalex.org/W2097117768","https://openalex.org/W2112796928","https://openalex.org/W2115579991","https://openalex.org/W2154740476","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2342642695","https://openalex.org/W2416791088","https://openalex.org/W2555618208","https://openalex.org/W2605364946","https://openalex.org/W2743905801","https://openalex.org/W2780038208","https://openalex.org/W2786431535","https://openalex.org/W2791003324","https://openalex.org/W2902314041","https://openalex.org/W2963432933","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6756834165"],"related_works":["https://openalex.org/W2802018156","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2969228573","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W2933098581","https://openalex.org/W4223892596","https://openalex.org/W2556125083"],"abstract_inverted_index":{"The":[0],"use":[1],"of":[2,59,71,191,202],"multiple":[3],"sensors":[4],"in":[5,12,34,121,132],"perception":[6],"systems":[7,38],"is":[8,19,91,119,213],"becoming":[9],"a":[10,83,127,175,216],"consensus":[11],"the":[13,20,48,69,133,143,193,222,232],"automotive":[14],"and":[15,26,36,63,67,75,99,102,139,148,160,227],"robotics":[16],"industries.":[17],"Camera":[18],"most":[21],"popular":[22],"technology,":[23],"however,":[24],"radar":[25],"LIDAR":[27,49,166],"are":[28,167,198,229],"increasingly":[29],"being":[30],"adopted":[31],"more":[32],"often":[33],"protection":[35,90],"safety":[37],"for":[39,93,109,231],"object/obstacle":[40],"detection.":[41],"In":[42,112,189],"this":[43,113],"paper,":[44],"we":[45],"particularly":[46],"explore":[47],"sensor":[50],"as":[51,130,181],"an":[52],"inter-modality":[53],"technology":[54],"which":[55,173],"provides":[56],"two":[57,154],"types":[58],"data,":[60],"range":[61],"(distance)":[62],"reflectance":[64,76,140],"(intensity":[65],"return),":[66],"study":[68],"influence":[70],"high-resolution":[72,170],"distance/depth":[73],"(DM)":[74],"maps":[77,172],"(RM)":[78],"on":[79,178,215],"pedestrian":[80,117],"classification":[81,118,212],"using":[82],"deep":[84],"Convolutional":[85],"Neural":[86],"Network":[87],"(CNN).":[88],"Pedestrian":[89,211],"critical":[92],"advanced":[94],"driver":[95],"assistance":[96],"system":[97],"(ADAS)":[98],"autonomous":[100],"driving,":[101],"it":[103],"has":[104],"regained":[105],"particular":[106],"attention":[107],"recently":[108],"known":[110],"reasons.":[111],"work,":[114],"CNN-LIDAR":[115],"based":[116],"studied":[120],"three":[122,233],"distinct":[123],"cases:":[124],"(i)":[125],"having":[126],"single":[128,182],"modality":[129],"input":[131],"CNN,":[134],"(ii)":[135],"by":[136,200],"combining":[137,150],"distance":[138],"measurements":[141],"at":[142],"CNN":[144],"input-level":[145],"(early":[146,186],"fusion),":[147],"(iii)":[149],"outputs":[151,194],"scores":[152],"from":[153,165,195,221],"single-modal":[155],"CNNs":[156,179,197],"(late":[157],"fusion).":[158],"Distance":[159],"intensity":[161],"(reflectance)":[162],"raw":[163],"data":[164],"transformed":[168],"to":[169],"(dense)":[171],"allow":[174],"direct":[176],"implementation":[177],"both":[180],"or":[183],"multi-channel":[184],"inputs":[185],"fusion":[187],"approach).":[188],"terms":[190],"late-fusion,":[192],"individual":[196],"combined":[199],"means":[201],"non-learning":[203],"rules,":[204],"such":[205],"as:":[206],"minimum,":[207],"maximum,":[208],"average,":[209],"product.":[210],"evaluated":[214],"`binary":[217],"classification'":[218],"dataset":[219],"created":[220],"KITTI":[223],"Vision":[224],"Benchmark":[225],"Suite,":[226],"results":[228],"shown":[230],"cases.":[234]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}