{"id":"https://openalex.org/W2786431535","doi":"https://doi.org/10.1109/apsipa.2017.8282301","title":"LiDAR/camera sensor fusion technology for pedestrian detection","display_name":"LiDAR/camera sensor fusion technology for pedestrian detection","publication_year":2017,"publication_date":"2017-12-01","ids":{"openalex":"https://openalex.org/W2786431535","doi":"https://doi.org/10.1109/apsipa.2017.8282301","mag":"2786431535"},"language":"en","primary_location":{"id":"doi:10.1109/apsipa.2017.8282301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2017.8282301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","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/A5108652424","display_name":"Tai-En Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Tai-En Wu","raw_affiliation_strings":["Department of Electronics Engineering and Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering and Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007783236","display_name":"Chia\u2013Chi Tsai","orcid":"https://orcid.org/0000-0002-2318-6376"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chia-Chi Tsai","raw_affiliation_strings":["Department of Electronics Engineering and Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering and Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5022312926","display_name":"Jiun-In Guo","orcid":"https://orcid.org/0000-0003-0402-2621"},"institutions":[{"id":"https://openalex.org/I148366613","display_name":"National Yang Ming Chiao Tung University","ror":"https://ror.org/00se2k293","country_code":"TW","type":"education","lineage":["https://openalex.org/I148366613"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Jiun-In Guo","raw_affiliation_strings":["Department of Electronics Engineering and Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics Engineering and Institute of Electronics, National Chiao-Tung University, Hsinchu, Taiwan","institution_ids":["https://openalex.org/I148366613"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.7916,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.9718845,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1675","last_page":"1678"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9923999905586243,"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"}},"topics":[{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9923999905586243,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9904000163078308,"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":0.8872711062431335},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.8188176155090332},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.776168942451477},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7266766428947449},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.726027250289917},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.7200665473937988},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5709900259971619},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4891951084136963},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4870872497558594},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.43662604689598083},{"id":"https://openalex.org/keywords/image-sensor","display_name":"Image sensor","score":0.42529192566871643},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.30845972895622253},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.22496867179870605},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1483558714389801},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07868948578834534}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8872711062431335},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.8188176155090332},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.776168942451477},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7266766428947449},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.726027250289917},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7200665473937988},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5709900259971619},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4891951084136963},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4870872497558594},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.43662604689598083},{"id":"https://openalex.org/C76935873","wikidata":"https://www.wikidata.org/wiki/Q209121","display_name":"Image sensor","level":2,"score":0.42529192566871643},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.30845972895622253},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.22496867179870605},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1483558714389801},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07868948578834534},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/apsipa.2017.8282301","is_oa":false,"landing_page_url":"https://doi.org/10.1109/apsipa.2017.8282301","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2020105253","https://openalex.org/W2057760367","https://openalex.org/W2114326338","https://openalex.org/W2244078817","https://openalex.org/W2327148878","https://openalex.org/W2415909907","https://openalex.org/W2511358806","https://openalex.org/W2515655118","https://openalex.org/W2547693818"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W3122828758","https://openalex.org/W2101960027","https://openalex.org/W4205958986","https://openalex.org/W2197846993","https://openalex.org/W49697837","https://openalex.org/W2586575957","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"Nowadays,":[0],"the":[1,28,48,52,75,79,89,123,131],"machine":[2],"learning":[3],"for":[4,35,126,140],"object":[5,53],"detection":[6,37,42,103,132,138],"is":[7,99,109],"growing":[8],"popular":[9],"and":[10,51,91,114,134],"widely":[11],"adopted":[12],"in":[13,59,102],"many":[14],"fields,":[15],"such":[16,96],"as":[17],"surveillance,":[18],"automotive,":[19],"passenger":[20],"flow":[21],"analysis,":[22],"etc.":[23],"This":[24],"paper":[25],"focuses":[26],"on":[27,78],"research":[29],"of":[30,93],"Lidar/camera":[31,84],"sensor":[32,72,85],"fusion":[33,86],"technology":[34],"pedestrian":[36,61,141],"to":[38,46,73,122],"ensure":[39],"extremely":[40],"high":[41],"accuracy.":[43],"In":[44],"order":[45],"reduce":[47],"false-positive":[49],"rate":[50,133],"occlusion":[54],"problem,":[55],"which":[56],"usually":[57],"happened":[58],"camera-based":[60],"detection,":[62],"we":[63],"use":[64],"3D":[65],"point":[66],"cloud":[67],"returning":[68],"from":[69,111,116],"Lidar":[70],"depth":[71],"do":[74],"further":[76],"examination":[77],"object's":[80],"shape.":[81],"The":[82,106],"proposed":[83],"design":[87],"complements":[88],"advantage":[90],"disadvantage":[92],"two":[94,117],"sensors":[95,118],"that":[97],"it":[98],"more":[100],"stable":[101],"than":[104],"others.":[105],"region":[107],"proposal":[108],"given":[110],"both":[112],"sensors,":[113],"candidate":[115],"are":[119],"also":[120],"going":[121],"second":[124],"classification":[125],"double":[127],"checking.":[128],"It":[129],"maximums":[130],"achieves":[135],"average":[136],"99.16%":[137],"accuracy":[139],"detection.":[142]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}