{"id":"https://openalex.org/W2419501904","doi":"https://doi.org/10.1109/icra.2016.7487382","title":"Detection of pedestrians at far distance","display_name":"Detection of pedestrians at far distance","publication_year":2016,"publication_date":"2016-05-01","ids":{"openalex":"https://openalex.org/W2419501904","doi":"https://doi.org/10.1109/icra.2016.7487382","mag":"2419501904"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2016.7487382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hal.science/hal-01297699","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004223779","display_name":"Rudy Bunel","orcid":"https://orcid.org/0009-0004-2036-6429"},"institutions":[{"id":"https://openalex.org/I102516824","display_name":"Universit\u00e9 de Technologie de Compi\u00e8gne","ror":"https://ror.org/04y5kwa70","country_code":"FR","type":"education","lineage":["https://openalex.org/I102516824"]},{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210088387","display_name":"Heuristics and Diagnostics for Complex Systems","ror":"https://ror.org/0075hvk77","country_code":"FR","type":"facility","lineage":["https://openalex.org/I102516824","https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210088387","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Rudy Bunel","raw_affiliation_strings":["Sorbonne Universit\u00e9s, Heudiasyc Laboratory, Compi\u00e8gne, France","Heudiasyc - Heuristique et Diagnostic des Syst\u00e8mes Complexes [Compi\u00e8gne] (UTC, CS 60319 - 57 avenue de Landshut - 60203 Compi\u00e8gne cedex - France)","Labex MS2T - Laboratoire d'Excellence 'Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques' (Universit\u00e9 de Technologie de Compi\u00e8gne - Centre de Recherche de Royallieu - rue du Docteur Schweitzer- CS 60319 - 60203 COMPIEGNE Cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sorbonne Universit\u00e9s, Heudiasyc Laboratory, Compi\u00e8gne, France","institution_ids":["https://openalex.org/I39804081","https://openalex.org/I4210088387"]},{"raw_affiliation_string":"Heudiasyc - Heuristique et Diagnostic des Syst\u00e8mes Complexes [Compi\u00e8gne] (UTC, CS 60319 - 57 avenue de Landshut - 60203 Compi\u00e8gne cedex - France)","institution_ids":["https://openalex.org/I102516824","https://openalex.org/I4210088387"]},{"raw_affiliation_string":"Labex MS2T - Laboratoire d'Excellence 'Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques' (Universit\u00e9 de Technologie de Compi\u00e8gne - Centre de Recherche de Royallieu - rue du Docteur Schweitzer- CS 60319 - 60203 COMPIEGNE Cedex - France)","institution_ids":["https://openalex.org/I102516824"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082916959","display_name":"Franck Davoine","orcid":"https://orcid.org/0000-0002-8587-6997"},"institutions":[{"id":"https://openalex.org/I102516824","display_name":"Universit\u00e9 de Technologie de Compi\u00e8gne","ror":"https://ror.org/04y5kwa70","country_code":"FR","type":"education","lineage":["https://openalex.org/I102516824"]},{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210088387","display_name":"Heuristics and Diagnostics for Complex Systems","ror":"https://ror.org/0075hvk77","country_code":"FR","type":"facility","lineage":["https://openalex.org/I102516824","https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210088387","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Franck Davoine","raw_affiliation_strings":["Sorbonne Universit\u00e9s, Heudiasyc Laboratory, Compi\u00e8gne, France","Heudiasyc - Heuristique et Diagnostic des Syst\u00e8mes Complexes [Compi\u00e8gne] (UTC, CS 60319 - 57 avenue de Landshut - 60203 Compi\u00e8gne cedex - France)","Labex MS2T - Laboratoire d'Excellence 'Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques' (Universit\u00e9 de Technologie de Compi\u00e8gne - Centre de Recherche de Royallieu - rue du Docteur Schweitzer- CS 60319 - 60203 COMPIEGNE Cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sorbonne Universit\u00e9s, Heudiasyc Laboratory, Compi\u00e8gne, France","institution_ids":["https://openalex.org/I39804081","https://openalex.org/I4210088387"]},{"raw_affiliation_string":"Heudiasyc - Heuristique et Diagnostic des Syst\u00e8mes Complexes [Compi\u00e8gne] (UTC, CS 60319 - 57 avenue de Landshut - 60203 Compi\u00e8gne cedex - France)","institution_ids":["https://openalex.org/I102516824","https://openalex.org/I4210088387"]},{"raw_affiliation_string":"Labex MS2T - Laboratoire d'Excellence 'Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques' (Universit\u00e9 de Technologie de Compi\u00e8gne - Centre de Recherche de Royallieu - rue du Docteur Schweitzer- CS 60319 - 60203 COMPIEGNE Cedex - France)","institution_ids":["https://openalex.org/I102516824"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049796269","display_name":"Philippe Xu","orcid":"https://orcid.org/0000-0001-7397-4808"},"institutions":[{"id":"https://openalex.org/I102516824","display_name":"Universit\u00e9 de Technologie de Compi\u00e8gne","ror":"https://ror.org/04y5kwa70","country_code":"FR","type":"education","lineage":["https://openalex.org/I102516824"]},{"id":"https://openalex.org/I39804081","display_name":"Sorbonne Universit\u00e9","ror":"https://ror.org/02en5vm52","country_code":"FR","type":"education","lineage":["https://openalex.org/I39804081"]},{"id":"https://openalex.org/I4210088387","display_name":"Heuristics and Diagnostics for Complex Systems","ror":"https://ror.org/0075hvk77","country_code":"FR","type":"facility","lineage":["https://openalex.org/I102516824","https://openalex.org/I1294671590","https://openalex.org/I1294671590","https://openalex.org/I4210088387","https://openalex.org/I4210159245"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Philippe Xu","raw_affiliation_strings":["Sorbonne Universit\u00e9s, Heudiasyc Laboratory, Compi\u00e8gne, France","Heudiasyc - Heuristique et Diagnostic des Syst\u00e8mes Complexes [Compi\u00e8gne] (UTC, CS 60319 - 57 avenue de Landshut - 60203 Compi\u00e8gne cedex - France)","Labex MS2T - Laboratoire d'Excellence 'Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques' (Universit\u00e9 de Technologie de Compi\u00e8gne - Centre de Recherche de Royallieu - rue du Docteur Schweitzer- CS 60319 - 60203 COMPIEGNE Cedex - France)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sorbonne Universit\u00e9s, Heudiasyc Laboratory, Compi\u00e8gne, France","institution_ids":["https://openalex.org/I39804081","https://openalex.org/I4210088387"]},{"raw_affiliation_string":"Heudiasyc - Heuristique et Diagnostic des Syst\u00e8mes Complexes [Compi\u00e8gne] (UTC, CS 60319 - 57 avenue de Landshut - 60203 Compi\u00e8gne cedex - France)","institution_ids":["https://openalex.org/I102516824","https://openalex.org/I4210088387"]},{"raw_affiliation_string":"Labex MS2T - Laboratoire d'Excellence 'Ma\u00eetrise des Syst\u00e8mes de Syst\u00e8mes Technologiques' (Universit\u00e9 de Technologie de Compi\u00e8gne - Centre de Recherche de Royallieu - rue du Docteur Schweitzer- CS 60319 - 60203 COMPIEGNE Cedex - France)","institution_ids":["https://openalex.org/I102516824"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8052,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.84967487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"2326","last_page":"2331"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9991000294685364,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.9405295848846436},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7968780994415283},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7807412147521973},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7171943187713623},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.70707768201828},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6252986788749695},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5702694058418274},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.48465877771377563},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.46398475766181946},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40020668506622314},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3897762894630432},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3827414810657501},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32126885652542114},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07933071255683899},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.062179386615753174}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.9405295848846436},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7968780994415283},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7807412147521973},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7171943187713623},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.70707768201828},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6252986788749695},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5702694058418274},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.48465877771377563},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.46398475766181946},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40020668506622314},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3897762894630432},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3827414810657501},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32126885652542114},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07933071255683899},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.062179386615753174},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra.2016.7487382","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2016.7487382","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:HAL:hal-01297699v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01297699","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"www.icra2016.org","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-01297699v1","is_oa":true,"landing_page_url":"https://hal.science/hal-01297699","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"www.icra2016.org","raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.6600000262260437,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W753012316","https://openalex.org/W1526734559","https://openalex.org/W1560380655","https://openalex.org/W1667652561","https://openalex.org/W1677182931","https://openalex.org/W1769599646","https://openalex.org/W1789336918","https://openalex.org/W1832500336","https://openalex.org/W1903127635","https://openalex.org/W1910108985","https://openalex.org/W1915068405","https://openalex.org/W1976818984","https://openalex.org/W1986905809","https://openalex.org/W1996901117","https://openalex.org/W1999853363","https://openalex.org/W2010340098","https://openalex.org/W2031454541","https://openalex.org/W2031489346","https://openalex.org/W2095705004","https://openalex.org/W2102605133","https://openalex.org/W2107775979","https://openalex.org/W2110439892","https://openalex.org/W2115579991","https://openalex.org/W2117687030","https://openalex.org/W2125556102","https://openalex.org/W2156547346","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2162741153","https://openalex.org/W2168356304","https://openalex.org/W2169671170","https://openalex.org/W2951183276","https://openalex.org/W2963367891","https://openalex.org/W2963542991","https://openalex.org/W4302296459","https://openalex.org/W6622239757","https://openalex.org/W6629368666","https://openalex.org/W6637151318","https://openalex.org/W6638020065","https://openalex.org/W6644092269","https://openalex.org/W6646904105","https://openalex.org/W6649495467","https://openalex.org/W6674330103"],"related_works":["https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2802018156","https://openalex.org/W4390721878","https://openalex.org/W2101531944","https://openalex.org/W4313315626","https://openalex.org/W2969228573","https://openalex.org/W2963690996","https://openalex.org/W2922437833","https://openalex.org/W4223892596"],"abstract_inverted_index":{"Pedestrian":[0,101],"detection":[1,36,47,70],"is":[2,37],"a":[3,83],"well-studied":[4],"problem.":[5],"Even":[6],"though":[7],"many":[8],"datasets":[9],"contain":[10],"challenging":[11],"case":[12,77],"studies,":[13],"the":[14,30,46,60,91,99],"performances":[15],"of":[16,25,32,48,78,116],"new":[17],"methods":[18,109],"are":[19,55,71],"often":[20],"only":[21],"reported":[22],"on":[23,45,98],"cases":[24],"reasonable":[26],"difficulty.":[27],"In":[28,40],"particular,":[29],"issue":[31],"small":[33,49],"scale":[34,50],"pedestrian":[35,69],"seldom":[38],"considered.":[39],"this":[41],"paper,":[42],"we":[43,81,106],"focus":[44],"pedestrians,":[51],"i.e.,":[52],"those":[53],"that":[54,64,105],"at":[56],"far":[57],"distance":[58],"from":[59],"camera.":[61],"We":[62],"show":[63],"classical":[65],"features":[66,92],"used":[67],"for":[68,75],"not":[72],"well":[73],"suited":[74],"our":[76],"study.":[79],"Instead,":[80],"propose":[82],"convolutional":[84],"neural":[85],"network":[86],"based":[87],"method":[88],"to":[89],"learn":[90],"with":[93],"an":[94],"end-to-end":[95],"approach.":[96],"Experiments":[97],"Caltech":[100],"Detection":[102],"Benchmark":[103],"showed":[104],"outperformed":[107],"existing":[108],"by":[110],"more":[111],"than":[112],"10%":[113],"in":[114],"terms":[115],"log-average":[117],"miss":[118],"rate.":[119]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":5}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}