{"id":"https://openalex.org/W2410859783","doi":"https://doi.org/10.1109/acpr.2015.7486528","title":"Camera and lidar fusion for pedestrian detection","display_name":"Camera and lidar fusion for pedestrian detection","publication_year":2015,"publication_date":"2015-11-01","ids":{"openalex":"https://openalex.org/W2410859783","doi":"https://doi.org/10.1109/acpr.2015.7486528","mag":"2410859783"},"language":"en","primary_location":{"id":"doi:10.1109/acpr.2015.7486528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","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/A5100384655","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0001-9223-2615"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wang Jun","raw_affiliation_strings":["National University of Defense Technology, Changsha, Hunan"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, Hunan","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101576187","display_name":"Tao Wu","orcid":"https://orcid.org/0000-0001-5245-3800"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Wu","raw_affiliation_strings":["National University of Defense Technology, Changsha, Hunan"],"affiliations":[{"raw_affiliation_string":"National University of Defense Technology, Changsha, Hunan","institution_ids":["https://openalex.org/I170215575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100384655"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.3682,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.71361093,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"371","last_page":"375"},"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/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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9986000061035156,"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/pedestrian-detection","display_name":"Pedestrian detection","score":0.8685213327407837},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7836678624153137},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7616312503814697},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7460524439811707},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.6995819807052612},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6700468063354492},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6559991836547852},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6277424097061157},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6027891039848328},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5329844355583191},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.5120802521705627},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4333973228931427},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4249758720397949},{"id":"https://openalex.org/keywords/probabilistic-classification","display_name":"Probabilistic classification","score":0.4209410548210144},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13468077778816223},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.10945042967796326},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.103802889585495},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06604945659637451}],"concepts":[{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8685213327407837},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7836678624153137},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7616312503814697},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7460524439811707},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.6995819807052612},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6700468063354492},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6559991836547852},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6277424097061157},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6027891039848328},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5329844355583191},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.5120802521705627},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4333973228931427},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4249758720397949},{"id":"https://openalex.org/C189119545","wikidata":"https://www.wikidata.org/wiki/Q5128022","display_name":"Probabilistic classification","level":4,"score":0.4209410548210144},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13468077778816223},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.10945042967796326},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.103802889585495},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06604945659637451},{"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acpr.2015.7486528","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acpr.2015.7486528","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6499999761581421}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2012860744","https://openalex.org/W2024046085","https://openalex.org/W2027280064","https://openalex.org/W2031454541","https://openalex.org/W2047860930","https://openalex.org/W2098699644","https://openalex.org/W2100242026","https://openalex.org/W2104511990","https://openalex.org/W2146036766","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2537159558","https://openalex.org/W3083767870","https://openalex.org/W3097096317"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W4243305035","https://openalex.org/W1537496349","https://openalex.org/W2972620127","https://openalex.org/W2981141433"],"abstract_inverted_index":{"The":[0],"aim":[1],"of":[2,35,57,92],"this":[3],"work":[4],"is":[5,74],"to":[6,16,79,110],"propose":[7,106],"a":[8,71,86,99,107,137],"fusion":[9],"procedure":[10],"based":[11],"on":[12,31,44,52,130],"lidar":[13,72],"and":[14,43,65,84],"camera":[15],"solve":[17],"the":[18,33,40,54,61,90,119],"pedestrian":[19,26,41,62,101,112,133],"detection":[20,27,63,113],"problem":[21],"in":[22,77,136],"autonomous":[23],"driving.":[24],"Current":[25],"algorithms":[28],"have":[29,123],"focused":[30],"improving":[32],"discriminability":[34],"2D":[36],"features":[37,83],"that":[38,95],"capture":[39],"appearance,":[42],"using":[45],"various":[46],"classifier":[47],"architectures.":[48],"However,":[49],"less":[50],"focus":[51],"exploiting":[53],"3D":[55],"structure":[56,82],"object":[58,81],"has":[59],"limited":[60],"performance":[64,126],"practicality.":[66],"To":[67],"tackle":[68],"these":[69],"issues,":[70],"subsystem":[73],"applied":[75],"here":[76],"order":[78],"extract":[80],"train":[85],"SVM":[87],"classifier,":[88],"reducing":[89],"number":[91],"candidate":[93],"windows":[94],"are":[96],"tested":[97],"by":[98,115],"state-of-the-art":[100,125],"appearance":[102],"classifier.":[103],"Additionally,":[104],"we":[105,122],"probabilistic":[108],"framework":[109],"fuse":[111],"given":[114],"both":[116],"subsystems.":[117],"With":[118],"proposed":[120],"framework,":[121],"achieved":[124],"at":[127],"20":[128],"fps":[129],"our":[131],"own":[132],"dataset":[134],"gathered":[135],"challenging":[138],"urban":[139],"scenario.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}