{"id":"https://openalex.org/W3021259687","doi":"https://doi.org/10.1109/socc46988.2019.1570558044","title":"FPGA-based object detection processor with HOG feature and SVM classifier","display_name":"FPGA-based object detection processor with HOG feature and SVM classifier","publication_year":2019,"publication_date":"2019-09-01","ids":{"openalex":"https://openalex.org/W3021259687","doi":"https://doi.org/10.1109/socc46988.2019.1570558044","mag":"3021259687"},"language":"en","primary_location":{"id":"doi:10.1109/socc46988.2019.1570558044","is_oa":false,"landing_page_url":"https://doi.org/10.1109/socc46988.2019.1570558044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 32nd IEEE International System-on-Chip Conference (SOCC)","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/A5069123107","display_name":"Fengwei An","orcid":"https://orcid.org/0000-0002-7554-7938"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fengwei An","raw_affiliation_strings":["Southern University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042204958","display_name":"Peng Xu","orcid":"https://orcid.org/0000-0003-3136-9188"},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Xu","raw_affiliation_strings":["Southern University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039273698","display_name":"Zhihua Xiao","orcid":null},"institutions":[{"id":"https://openalex.org/I3045169105","display_name":"Southern University of Science and Technology","ror":"https://ror.org/049tv2d57","country_code":"CN","type":"education","lineage":["https://openalex.org/I3045169105"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihua Xiao","raw_affiliation_strings":["Southern University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Southern University of Science and Technology","institution_ids":["https://openalex.org/I3045169105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100406890","display_name":"Chao Wang","orcid":"https://orcid.org/0000-0001-7398-1127"},"institutions":[{"id":"https://openalex.org/I47720641","display_name":"Huazhong University of Science and Technology","ror":"https://ror.org/00p991c53","country_code":"CN","type":"education","lineage":["https://openalex.org/I47720641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Wang","raw_affiliation_strings":["Huazhong University of Science and Technology"],"affiliations":[{"raw_affiliation_string":"Huazhong University of Science and Technology","institution_ids":["https://openalex.org/I47720641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5069123107"],"corresponding_institution_ids":["https://openalex.org/I3045169105"],"apc_list":null,"apc_paid":null,"fwci":0.6073,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.73664622,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"187","last_page":"190"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9994000196456909,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9994000196456909,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9991999864578247,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.996399998664856,"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":0.8518093824386597},{"id":"https://openalex.org/keywords/histogram-of-oriented-gradients","display_name":"Histogram of oriented gradients","score":0.7627347111701965},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7600659132003784},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7429943084716797},{"id":"https://openalex.org/keywords/sliding-window-protocol","display_name":"Sliding window protocol","score":0.6800881624221802},{"id":"https://openalex.org/keywords/field-programmable-gate-array","display_name":"Field-programmable gate array","score":0.651743471622467},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.6344426870346069},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5739482641220093},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5528994798660278},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5333461761474609},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5297086238861084},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4739815294742584},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.4643927216529846},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.44576138257980347},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.23181402683258057},{"id":"https://openalex.org/keywords/window","display_name":"Window (computing)","score":0.19167819619178772},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09573575854301453},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.08221131563186646}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8518093824386597},{"id":"https://openalex.org/C17426736","wikidata":"https://www.wikidata.org/wiki/Q419918","display_name":"Histogram of oriented gradients","level":4,"score":0.7627347111701965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7600659132003784},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7429943084716797},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.6800881624221802},{"id":"https://openalex.org/C42935608","wikidata":"https://www.wikidata.org/wiki/Q190411","display_name":"Field-programmable gate array","level":2,"score":0.651743471622467},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6344426870346069},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5739482641220093},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5528994798660278},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5333461761474609},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5297086238861084},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4739815294742584},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.4643927216529846},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.44576138257980347},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.23181402683258057},{"id":"https://openalex.org/C2778751112","wikidata":"https://www.wikidata.org/wiki/Q835016","display_name":"Window (computing)","level":2,"score":0.19167819619178772},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09573575854301453},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08221131563186646},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/socc46988.2019.1570558044","is_oa":false,"landing_page_url":"https://doi.org/10.1109/socc46988.2019.1570558044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 32nd IEEE International System-on-Chip Conference (SOCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.4300000071525574,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":9,"referenced_works":["https://openalex.org/W2026757773","https://openalex.org/W2074091263","https://openalex.org/W2161969291","https://openalex.org/W2257868038","https://openalex.org/W2570343428","https://openalex.org/W2735706032","https://openalex.org/W2792296420","https://openalex.org/W2912691033","https://openalex.org/W6740979191"],"related_works":["https://openalex.org/W2112097167","https://openalex.org/W2352028961","https://openalex.org/W2124097254","https://openalex.org/W2802018156","https://openalex.org/W2142173439","https://openalex.org/W2400994047","https://openalex.org/W1977648593","https://openalex.org/W2348780717","https://openalex.org/W2101531944","https://openalex.org/W2556125083"],"abstract_inverted_index":{"Computer":[0],"vision":[1],"is":[2,18,46,168,173],"an":[3,48,74],"important":[4],"sensing":[5],"technique":[6],"to":[7,11,22,149],"translate":[8],"the":[9,79,115,132,137,165,183],"information":[10],"decisions.":[12],"In":[13,69],"robotic":[14],"applications,":[15],"object":[16,75,161],"detection":[17,76],"a":[19,52,58,85],"critical":[20],"skill":[21],"perform":[23],"tasks":[24],"for":[25,51,119,182],"robots":[26],"in":[27,82,124,140,156,163],"complex":[28],"environments.":[29],"The":[30,105,128,159],"deep-learning":[31],"framework,":[32],"e.g.":[33],"You":[34],"Only":[35],"Look":[36],"Once":[37],"(YOLO),":[38],"attracts":[39],"much":[40],"more":[41],"attention":[42],"recently.":[43],"Moreover,":[44],"it":[45,56],"not":[47],"optimal":[49],"solution":[50],"mobile":[53],"robot":[54],"since":[55],"requires":[57],"large":[59],"scale":[60],"of":[61,88],"hardware":[62],"resources,":[63],"on-chip":[64],"SRAMs,":[65],"and":[66,94,152,175],"power":[67],"consumption.":[68],"this":[70],"work,":[71],"we":[72],"report":[73],"processor":[77],"synchronizing":[78],"image":[80],"sensor":[81],"FPGA":[83,181],"with":[84,110],"cellbased":[86],"histogram":[87],"oriented":[89],"gradient":[90],"(HOG)":[91],"feature":[92,107,117],"descriptor":[93],"support":[95],"vector":[96,138],"machine":[97],"(SVM)":[98],"classifier":[99,167],"by":[100,170],"parallel":[101],"sliding":[102,126,142],"window":[103],"mechanism.":[104],"HOG":[106],"extraction":[108],"circuitry":[109],"pixel-based":[111],"pipelined":[112],"architecture":[113],"constructs":[114],"cell-based":[116],"vectors":[118],"parallelizing":[120],"partial":[121],"SVM-based":[122],"classification":[123,130],"multiple":[125,154],"windows.":[127],"SVM":[129,166],"produces":[131],"final":[133],"result":[134],"after":[135],"accumulating":[136],"components":[139],"one":[141],"window.":[143],"This":[144],"framework":[145],"can":[146],"be":[147],"used":[148],"both":[150],"localize":[151],"recognize":[153],"objects":[155],"video":[157],"footage.":[158],"proposed":[160],"processor,":[162],"which":[164],"trained":[169],"INRIA":[171],"datasets,":[172],"implemented":[174],"verified":[176],"on":[177],"Intel":[178],"Stratix":[179],"IV":[180],"pedestrian.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
