{"id":"https://openalex.org/W2783791736","doi":"https://doi.org/10.1109/asicon.2017.8252559","title":"Object-recognition VLSI for pedestrian detection in automotive applications","display_name":"Object-recognition VLSI for pedestrian detection in automotive applications","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2783791736","doi":"https://doi.org/10.1109/asicon.2017.8252559","mag":"2783791736"},"language":"en","primary_location":{"id":"doi:10.1109/asicon.2017.8252559","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asicon.2017.8252559","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 12th International Conference on ASIC (ASICON)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5069123107","display_name":"Fengwei An","orcid":"https://orcid.org/0000-0002-7554-7938"},"institutions":[{"id":"https://openalex.org/I183792356","display_name":"Hiroshima University of Economics","ror":"https://ror.org/027b58k10","country_code":"JP","type":"education","lineage":["https://openalex.org/I183792356"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Fengwei An","raw_affiliation_strings":["Hiroshima University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hiroshima University, Japan","institution_ids":["https://openalex.org/I183792356"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100362471","display_name":"Xiangyu Zhang","orcid":"https://orcid.org/0000-0003-3716-4722"},"institutions":[{"id":"https://openalex.org/I183792356","display_name":"Hiroshima University of Economics","ror":"https://ror.org/027b58k10","country_code":"JP","type":"education","lineage":["https://openalex.org/I183792356"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Xiangyu Zhang","raw_affiliation_strings":["Hiroshima University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hiroshima University, Japan","institution_ids":["https://openalex.org/I183792356"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100333575","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0003-3568-4468"},"institutions":[{"id":"https://openalex.org/I183792356","display_name":"Hiroshima University of Economics","ror":"https://ror.org/027b58k10","country_code":"JP","type":"education","lineage":["https://openalex.org/I183792356"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["Hiroshima University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hiroshima University, Japan","institution_ids":["https://openalex.org/I183792356"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060719224","display_name":"Idaku Ishii","orcid":"https://orcid.org/0000-0002-7728-2363"},"institutions":[{"id":"https://openalex.org/I183792356","display_name":"Hiroshima University of Economics","ror":"https://ror.org/027b58k10","country_code":"JP","type":"education","lineage":["https://openalex.org/I183792356"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Idaku Ishii","raw_affiliation_strings":["Hiroshima University, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hiroshima University, Japan","institution_ids":["https://openalex.org/I183792356"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183792356"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"651","last_page":"653"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9943000078201294,"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":0.9923999905586243,"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/computer-science","display_name":"Computer science","score":0.7672889232635498},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.6994783878326416},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6873664855957031},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6844698190689087},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.668667733669281},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.6318916082382202},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6216539740562439},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5447098612785339},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5427290797233582},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5418785810470581},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5409492254257202},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5374669432640076},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.497527152299881},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.49073970317840576},{"id":"https://openalex.org/keywords/coprocessor","display_name":"Coprocessor","score":0.4749048948287964},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.47142118215560913},{"id":"https://openalex.org/keywords/very-large-scale-integration","display_name":"Very-large-scale integration","score":0.4550824761390686},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.31782644987106323},{"id":"https://openalex.org/keywords/computer-hardware","display_name":"Computer hardware","score":0.17763444781303406},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.15716058015823364},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.12606105208396912},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11696451902389526},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.10401859879493713}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7672889232635498},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.6994783878326416},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6873664855957031},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6844698190689087},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.668667733669281},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.6318916082382202},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6216539740562439},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5447098612785339},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5427290797233582},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5418785810470581},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5409492254257202},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5374669432640076},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.497527152299881},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.49073970317840576},{"id":"https://openalex.org/C86111242","wikidata":"https://www.wikidata.org/wiki/Q859595","display_name":"Coprocessor","level":2,"score":0.4749048948287964},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.47142118215560913},{"id":"https://openalex.org/C14580979","wikidata":"https://www.wikidata.org/wiki/Q876049","display_name":"Very-large-scale integration","level":2,"score":0.4550824761390686},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.31782644987106323},{"id":"https://openalex.org/C9390403","wikidata":"https://www.wikidata.org/wiki/Q3966","display_name":"Computer hardware","level":1,"score":0.17763444781303406},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.15716058015823364},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.12606105208396912},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11696451902389526},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.10401859879493713},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/asicon.2017.8252559","is_oa":false,"landing_page_url":"https://doi.org/10.1109/asicon.2017.8252559","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE 12th International Conference on ASIC (ASICON)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.46000000834465027,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W2081021369","https://openalex.org/W2161969291","https://openalex.org/W2570343428","https://openalex.org/W3097096317"],"related_works":["https://openalex.org/W2352028961","https://openalex.org/W2124097254","https://openalex.org/W1977648593","https://openalex.org/W2348780717","https://openalex.org/W2780165746","https://openalex.org/W3026806648","https://openalex.org/W2602033589","https://openalex.org/W4235475963","https://openalex.org/W4252030899","https://openalex.org/W2374077716"],"abstract_inverted_index":{"We":[0],"report":[1],"a":[2,18,36,56,96],"VLSI":[3],"implementation":[4,98],"object-recognition":[5],"coprocessor":[6],"exploiting":[7],"both":[8],"histogram":[9],"of":[10,90,99],"oriented":[11],"gradient":[12],"(HOG)":[13],"and":[14,30,83],"Haar-like":[15,31,84],"descriptors":[16,32],"with":[17,106],"cell-based":[19,57],"parallel":[20],"sliding-window":[21],"recognition":[22,63],"mechanism.":[23],"The":[24,71,87],"feature":[25,54,85,93],"extraction":[26],"circuitry":[27],"for":[28,64,103],"HOG":[29,82],"are":[33],"implemented":[34],"by":[35],"pixel-based":[37],"pipelined":[38],"architecture,":[39],"which":[40,67],"synchronizes":[41],"to":[42,80],"the":[43,47,81,91,100],"pixel":[44],"frequency":[45],"from":[46],"image":[48],"sensor.":[49],"After":[50],"extracting":[51],"each":[52],"cell":[53],"vector,":[55],"sliding":[58],"window":[59],"scheme":[60],"enables":[61],"parallelized":[62],"all":[65],"windows,":[66],"contain":[68],"this":[69],"cell.":[70],"nearest":[72],"neighbor":[73],"search":[74],"(NNS)":[75],"classifier":[76],"is":[77],"respectively":[78],"applied":[79],"space.":[86],"complementary":[88],"aspects":[89],"two":[92],"domains":[94],"enable":[95],"hardware-friendly":[97],"binary":[101],"classification":[102],"pedestrian":[104],"detection":[105],"improved":[107],"accuracy.":[108]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}