{"id":"https://openalex.org/W2766048335","doi":"https://doi.org/10.1109/avss.2017.8078512","title":"Combining LiDAR space clustering and convolutional neural networks for pedestrian detection","display_name":"Combining LiDAR space clustering and convolutional neural networks for pedestrian detection","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2766048335","doi":"https://doi.org/10.1109/avss.2017.8078512","mag":"2766048335"},"language":"en","primary_location":{"id":"doi:10.1109/avss.2017.8078512","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss.2017.8078512","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1710.06160","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5029916016","display_name":"Damien Matti","orcid":null},"institutions":[{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Damien Matti","raw_affiliation_strings":["LTS5, EPFL, Lausanne, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LTS5, EPFL, Lausanne, Switzerland","institution_ids":["https://openalex.org/I5124864"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009982931","display_name":"Haz\u0131m Kemal Ekenel","orcid":"https://orcid.org/0000-0003-3697-8548"},"institutions":[{"id":"https://openalex.org/I48912391","display_name":"Istanbul Technical University","ror":"https://ror.org/059636586","country_code":"TR","type":"education","lineage":["https://openalex.org/I48912391"]},{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH","TR"],"is_corresponding":false,"raw_author_name":"Hazim Kemal Ekenel","raw_affiliation_strings":["LTS5, EPFL, Lausanne, Switzerland","SiMiT Lab, ITU, Istanbul, Turkey"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LTS5, EPFL, Lausanne, Switzerland","institution_ids":["https://openalex.org/I5124864"]},{"raw_affiliation_string":"SiMiT Lab, ITU, Istanbul, Turkey","institution_ids":["https://openalex.org/I48912391"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067782007","display_name":"Jean\u2010Philippe Thiran","orcid":"https://orcid.org/0000-0003-2938-9657"},"institutions":[{"id":"https://openalex.org/I5124864","display_name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","ror":"https://ror.org/02s376052","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I5124864"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Jean-Philippe Thiran","raw_affiliation_strings":["LTS5, EPFL, Lausanne, Switzerland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LTS5, EPFL, Lausanne, Switzerland","institution_ids":["https://openalex.org/I5124864"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9994000196456909,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.8306350708007812},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7673888206481934},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7664324641227722},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.741665244102478},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6499553918838501},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6309319138526917},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5960366725921631},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5258158445358276},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.4976637661457062},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4812064468860626},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42945343255996704},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4256175756454468},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4210337996482849},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38481688499450684},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32184725999832153},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.24235409498214722},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13123053312301636}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8306350708007812},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7673888206481934},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7664324641227722},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.741665244102478},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6499553918838501},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6309319138526917},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5960366725921631},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5258158445358276},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.4976637661457062},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4812064468860626},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42945343255996704},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4256175756454468},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4210337996482849},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38481688499450684},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32184725999832153},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.24235409498214722},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13123053312301636},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/avss.2017.8078512","is_oa":false,"landing_page_url":"https://doi.org/10.1109/avss.2017.8078512","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1710.06160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1710.06160","pdf_url":"https://arxiv.org/pdf/1710.06160","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":null,"raw_type":"text"},{"id":"mag:2766048335","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1710.06160","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":"pmh:oai:infoscience.epfl.ch:232392","is_oa":true,"landing_page_url":"http://infoscience.epfl.ch/record/232392","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Text"},{"id":"pmh:oai:infoscience.tind.io:232392","is_oa":true,"landing_page_url":"https://infoscience.epfl.ch/handle/20.500.14299/142115","pdf_url":null,"source":{"id":"https://openalex.org/S4306400487","display_name":"Infoscience (Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"conference proceedings"},{"id":"doi:10.48550/arxiv.1710.06160","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1710.06160","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":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1710.06160","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1710.06160","pdf_url":"https://arxiv.org/pdf/1710.06160","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":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7099999785423279,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1673310716","https://openalex.org/W1773136482","https://openalex.org/W1966456026","https://openalex.org/W2085261163","https://openalex.org/W2096166026","https://openalex.org/W2102605133","https://openalex.org/W2105954061","https://openalex.org/W2117539524","https://openalex.org/W2118021859","https://openalex.org/W2133775549","https://openalex.org/W2150066425","https://openalex.org/W2157468226","https://openalex.org/W2562674605","https://openalex.org/W2580683366","https://openalex.org/W2949650786","https://openalex.org/W2949847849","https://openalex.org/W2951638509","https://openalex.org/W2953106684","https://openalex.org/W6620707391","https://openalex.org/W6636787326","https://openalex.org/W6677786781","https://openalex.org/W6682132143","https://openalex.org/W6687483927","https://openalex.org/W6730526001","https://openalex.org/W6735361214"],"related_works":["https://openalex.org/W2964184568","https://openalex.org/W2905005563","https://openalex.org/W2558093053","https://openalex.org/W2588319624","https://openalex.org/W2955045803","https://openalex.org/W3170944651","https://openalex.org/W2601916599","https://openalex.org/W2962912109","https://openalex.org/W2889480448","https://openalex.org/W3084154669","https://openalex.org/W2952499365","https://openalex.org/W2899178670","https://openalex.org/W3003437478","https://openalex.org/W2596750703","https://openalex.org/W3129877547","https://openalex.org/W2429834910","https://openalex.org/W2809783273","https://openalex.org/W3036025792","https://openalex.org/W2549143551","https://openalex.org/W3026181888"],"abstract_inverted_index":{"Pedestrian":[0],"detection":[1,46,62,138],"is":[2,97],"an":[3],"important":[4],"component":[5],"for":[6,14,79,129,172,184],"safety":[7],"of":[8,160],"autonomous":[9,80],"vehicles,":[10],"as":[11,13],"well":[12],"traffic":[15],"and":[16,26,59,169],"street":[17],"surveillance.":[18],"There":[19],"are":[20,116],"extensive":[21],"benchmarks":[22],"on":[23,38,140],"this":[24,71],"topic":[25],"it":[27,111],"has":[28],"been":[29,52,65],"shown":[30],"to":[31,88,99,165],"be":[32],"a":[33,75,121,156],"challenging":[34],"problem":[35],"when":[36],"applied":[37],"real":[39],"use-case":[40],"scenarios.":[41],"In":[42,70,91],"purely":[43],"image-based":[44],"pedestrian":[45,77,130,173],"approaches,":[47],"the":[48,92,105,136,141,149],"state-of-the-art":[49,122],"results":[50,146],"have":[51,64,127,133],"achieved":[53],"with":[54],"convolutional":[55],"neural":[56],"networks":[57],"(CNN)":[58],"surprisingly":[60],"few":[61],"frameworks":[63],"built":[66],"upon":[67],"multi-cue":[68],"approaches.":[69,186],"work,":[72],"we":[73,126],"develop":[74],"new":[76],"detector":[78],"vehicles":[81],"that":[82,110,125,148,177],"exploits":[83],"LiDAR":[84,95,151,178],"data,":[85],"in":[86],"addition":[87],"visual":[89],"information.":[90],"proposed":[93,137,150],"approach,":[94],"data":[96,179],"utilized":[98],"generate":[100],"region":[101,162],"proposals":[102,163],"by":[103,120],"processing":[104],"three":[106],"dimensional":[107],"point":[108],"cloud":[109],"provides.":[112],"These":[113],"candidate":[114],"regions":[115],"then":[117],"further":[118],"processed":[119],"CNN":[123],"classifier":[124],"fine-tuned":[128],"detection.":[131,174],"We":[132],"extensively":[134],"evaluated":[135],"process":[139],"KITTI":[142],"dataset.":[143],"The":[144],"experimental":[145],"show":[147],"space":[152],"clustering":[153],"approach":[154],"provides":[155],"very":[157],"efficient":[158],"way":[159],"generating":[161],"leading":[164],"higher":[166],"recall":[167],"rates":[168],"fewer":[170],"misses":[171],"This":[175],"indicates":[176],"can":[180],"provide":[181],"auxiliary":[182],"information":[183],"CNN-based":[185]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
