{"id":"https://openalex.org/W3036890019","doi":"https://doi.org/10.1109/itsc45102.2020.9294537","title":"LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB","display_name":"LRPD: Long Range 3D Pedestrian Detection Leveraging Specific Strengths of LiDAR and RGB","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3036890019","doi":"https://doi.org/10.1109/itsc45102.2020.9294537","mag":"3036890019"},"language":"en","primary_location":{"id":"doi:10.1109/itsc45102.2020.9294537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294537","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://export.arxiv.org/pdf/2006.09738","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074907743","display_name":"Michael F\u00fcrst","orcid":"https://orcid.org/0000-0001-6647-5031"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Michael Furst","raw_affiliation_strings":["DFKI - German Research Center for Artificial Intelligence","German Research Centre for Artificial Intelligence"],"affiliations":[{"raw_affiliation_string":"DFKI - German Research Center for Artificial Intelligence","institution_ids":["https://openalex.org/I33256026"]},{"raw_affiliation_string":"German Research Centre for Artificial Intelligence","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020732874","display_name":"Oliver Wasenm\u00fcller","orcid":null},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Oliver Wasenmuller","raw_affiliation_strings":["DFKI - German Research Center for Artificial Intelligence"],"affiliations":[{"raw_affiliation_string":"DFKI - German Research Center for Artificial Intelligence","institution_ids":["https://openalex.org/I33256026"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051650277","display_name":"Didier Stricker","orcid":"https://orcid.org/0009-0004-8794-6858"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Didier Stricker","raw_affiliation_strings":["DFKI - German Research Center for Artificial Intelligence"],"affiliations":[{"raw_affiliation_string":"DFKI - German Research Center for Artificial Intelligence","institution_ids":["https://openalex.org/I33256026"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5074907743"],"corresponding_institution_ids":["https://openalex.org/I33256026"],"apc_list":null,"apc_paid":null,"fwci":0.31490195,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.55648114,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991000294685364,"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"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9977999925613403,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.8542303442955017},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8263925313949585},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.7150634527206421},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.683374285697937},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6815187931060791},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6701644659042358},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6655763387680054},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6477103233337402},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5571397542953491},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.5341507196426392},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.43206220865249634},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.27255725860595703},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21483036875724792},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.18544015288352966},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.11436715722084045},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10469275712966919},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.060976624488830566}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8542303442955017},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8263925313949585},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.7150634527206421},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.683374285697937},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6815187931060791},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6701644659042358},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6655763387680054},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6477103233337402},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5571397542953491},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.5341507196426392},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.43206220865249634},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.27255725860595703},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21483036875724792},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.18544015288352966},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.11436715722084045},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10469275712966919},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.060976624488830566},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/itsc45102.2020.9294537","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294537","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},{"id":"mag:3036890019","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2006.09738","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2006.09738","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2006.09738","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"}],"best_oa_location":{"id":"mag:3036890019","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/2006.09738","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6299999952316284,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2115579991","https://openalex.org/W2150066425","https://openalex.org/W2194775991","https://openalex.org/W2531409750","https://openalex.org/W2560609797","https://openalex.org/W2565639579","https://openalex.org/W2612445135","https://openalex.org/W2613718673","https://openalex.org/W2919534447","https://openalex.org/W2949708697","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963400571","https://openalex.org/W2963446712","https://openalex.org/W2963727135","https://openalex.org/W2964062501","https://openalex.org/W2968296999","https://openalex.org/W2981949127","https://openalex.org/W2997814983","https://openalex.org/W3004148335","https://openalex.org/W3004237909","https://openalex.org/W3004414032","https://openalex.org/W3106250896","https://openalex.org/W3121091114","https://openalex.org/W6620707391","https://openalex.org/W6637373629","https://openalex.org/W6737664043","https://openalex.org/W6756751353","https://openalex.org/W6761628715","https://openalex.org/W6763422710","https://openalex.org/W6771543527","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W3115753462","https://openalex.org/W2318314190","https://openalex.org/W2424637586","https://openalex.org/W2872491670","https://openalex.org/W3127699632","https://openalex.org/W3200790673","https://openalex.org/W3183519347","https://openalex.org/W2415909907","https://openalex.org/W3161950902","https://openalex.org/W2895865366","https://openalex.org/W2937753789","https://openalex.org/W2806837250","https://openalex.org/W2914169852","https://openalex.org/W2088271606","https://openalex.org/W3000171824","https://openalex.org/W2791134985","https://openalex.org/W2955573317","https://openalex.org/W3048391456","https://openalex.org/W2333465136","https://openalex.org/W2412490945"],"abstract_inverted_index":{"While":[0],"short":[1],"range":[2,12,51,100],"3D":[3,52],"pedestrian":[4,53],"detection":[5,54],"is":[6],"sufficient":[7],"for":[8,16,67],"emergency":[9],"breaking,":[10],"long":[11,41,50,99],"detections":[13],"are":[14,78],"required":[15],"smooth":[17],"breaking":[18],"and":[19,61,72],"gaining":[20],"trust":[21],"in":[22,34,96],"autonomous":[23],"vehicles.":[24],"The":[25,107],"current":[26,104],"state-of-the-art":[27],"on":[28,98,115],"the":[29,36,57,62,103,116,119],"KITTI":[30,120],"benchmark":[31],"performs":[32],"suboptimal":[33],"detecting":[35],"position":[37],"of":[38,59,64,109],"pedestrians":[39,117],"at":[40],"range.":[42],"Thus,":[43],"we":[44],"propose":[45],"an":[46],"approach":[47,112],"specifically":[48],"targeting":[49],"(LRPD),":[55],"leveraging":[56],"density":[58],"RGB":[60,69],"precision":[63],"LiDAR.":[65],"Therefore,":[66],"proposals,":[68],"instance":[70],"segmentation":[71],"LiDAR":[73],"point":[74],"based":[75],"proposal":[76],"generation":[77],"combined,":[79],"followed":[80],"by":[81],"a":[82,93],"second":[83],"stage":[84],"using":[85],"both":[86],"sensor":[87],"modalities":[88],"symmetrically.":[89],"This":[90],"leads":[91],"to":[92,102],"significant":[94],"improvement":[95],"mAP":[97],"compared":[101],"state-of-the":[105],"art.":[106],"evaluation":[108],"our":[110],"LRPD":[111],"was":[113],"done":[114],"from":[118],"benchmark.":[121]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}