{"id":"https://openalex.org/W2892145335","doi":"https://doi.org/10.1109/aipr.2017.8457954","title":"Robust and Accurate Objects Measurement in Real-World Based on Camera System","display_name":"Robust and Accurate Objects Measurement in Real-World Based on Camera System","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2892145335","doi":"https://doi.org/10.1109/aipr.2017.8457954","mag":"2892145335"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2017.8457954","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457954","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5103419663","display_name":"Asaad F. Said","orcid":null},"institutions":[{"id":"https://openalex.org/I1343180700","display_name":"Intel (United States)","ror":"https://ror.org/01ek73717","country_code":"US","type":"company","lineage":["https://openalex.org/I1343180700"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Asaad F. Said","raw_affiliation_strings":["Intel Corporation, Chandler, USA"],"affiliations":[{"raw_affiliation_string":"Intel Corporation, Chandler, USA","institution_ids":["https://openalex.org/I1343180700"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5103419663"],"corresponding_institution_ids":["https://openalex.org/I1343180700"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.30677556,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"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":0.9975000023841858,"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/computer-vision","display_name":"Computer vision","score":0.8247619271278381},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7414398193359375},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.7266465425491333},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.7168098092079163},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.657983660697937},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6445642709732056},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6166917085647583},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5432125926017761},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5394739508628845},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5023400783538818},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4207199811935425},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26476138830184937},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22381645441055298},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.09308350086212158}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.8247619271278381},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7414398193359375},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.7266465425491333},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.7168098092079163},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.657983660697937},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6445642709732056},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6166917085647583},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5432125926017761},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5394739508628845},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5023400783538818},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4207199811935425},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26476138830184937},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22381645441055298},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.09308350086212158},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr.2017.8457954","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457954","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1553500174","https://openalex.org/W2102049273","https://openalex.org/W2105267250","https://openalex.org/W2120010140","https://openalex.org/W2167667767","https://openalex.org/W2395130813","https://openalex.org/W2553321288","https://openalex.org/W3216042775","https://openalex.org/W6712309770","https://openalex.org/W6804029030"],"related_works":["https://openalex.org/W2974375613","https://openalex.org/W2030712947","https://openalex.org/W2489489317","https://openalex.org/W1564558219","https://openalex.org/W3002118011","https://openalex.org/W4288043240","https://openalex.org/W2801801420","https://openalex.org/W2011792403","https://openalex.org/W4312423884","https://openalex.org/W3113327026"],"abstract_inverted_index":{"Object's":[0],"dimension":[1,35,84],"and":[2,14,23,38,56,79,96],"its":[3],"proximity":[4],"in":[5,11,17,28,40,71,85],"real-world":[6,41,86],"plays":[7],"a":[8],"critical":[9],"role":[10],"safe":[12],"navigation":[13],"collision":[15],"avoidance":[16],"autonomous":[18],"cars.":[19],"An":[20],"accurate,":[21],"reliable,":[22],"cost-effective":[24],"approach":[25,77],"was":[26],"developed":[27],"this":[29],"paper":[30],"to":[31,51],"measure":[32,53,81],"the":[33,64,72,75,82,99],"object's":[34,83],"(distance,":[36],"width,":[37],"height)":[39],"solely":[42],"based":[43],"on":[44],"camera":[45,59],"system.":[46],"Mathematical":[47],"representations":[48],"were":[49,94],"derived":[50,92],"accurately":[52,80],"object":[54,70],"dimensions":[55],"extract":[57],"extrinsic":[58],"parameters":[60],"while":[61],"driving.":[62],"Giving":[63],"bounding":[65],"box":[66],"coordinates":[67],"around":[68],"each":[69],"captured":[73],"frame,":[74],"proposed":[76],"automatically":[78],"(ft.)":[87],"instead":[88],"of":[89],"pixels.":[90],"The":[91],"models":[93],"verified":[95],"tested":[97],"against":[98],"ground":[100],"truth":[101],"data":[102],"which":[103],"showed":[104],"strong":[105],"correlation.":[106]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}