{"id":"https://openalex.org/W2790984575","doi":"https://doi.org/10.1109/tvlsi.2018.2808104","title":"Energy-Efficient Pedestrian Detection System: Exploiting Statistical Error Compensation for Lossy Memory Data Compression","display_name":"Energy-Efficient Pedestrian Detection System: Exploiting Statistical Error Compensation for Lossy Memory Data Compression","publication_year":2018,"publication_date":"2018-03-09","ids":{"openalex":"https://openalex.org/W2790984575","doi":"https://doi.org/10.1109/tvlsi.2018.2808104","mag":"2790984575"},"language":"en","primary_location":{"id":"doi:10.1109/tvlsi.2018.2808104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvlsi.2018.2808104","pdf_url":null,"source":{"id":"https://openalex.org/S37538908","display_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","issn_l":"1063-8210","issn":["1063-8210","1557-9999"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","raw_type":"journal-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/A5014075867","display_name":"Yinqi Tang","orcid":"https://orcid.org/0000-0001-6667-1833"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yinqi Tang","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101645607","display_name":"Naveen Verma","orcid":"https://orcid.org/0000-0002-8208-5030"},"institutions":[{"id":"https://openalex.org/I20089843","display_name":"Princeton University","ror":"https://ror.org/00hx57361","country_code":"US","type":"education","lineage":["https://openalex.org/I20089843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Naveen Verma","raw_affiliation_strings":["Department of Electrical Engineering, Princeton University, Princeton, NJ, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Princeton University, Princeton, NJ, USA","institution_ids":["https://openalex.org/I20089843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014075867"],"corresponding_institution_ids":["https://openalex.org/I20089843"],"apc_list":null,"apc_paid":null,"fwci":1.0446,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.81741973,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"26","issue":"7","first_page":"1301","last_page":"1311"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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":0.9997000098228455,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9987999796867371,"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-science","display_name":"Computer science","score":0.7606527209281921},{"id":"https://openalex.org/keywords/lossy-compression","display_name":"Lossy compression","score":0.5961121916770935},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5057721138000488},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4320800304412842},{"id":"https://openalex.org/keywords/data-compression","display_name":"Data compression","score":0.43152570724487305},{"id":"https://openalex.org/keywords/histogram","display_name":"Histogram","score":0.42825746536254883},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.12178170680999756},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11902880668640137}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7606527209281921},{"id":"https://openalex.org/C165021410","wikidata":"https://www.wikidata.org/wiki/Q55564","display_name":"Lossy compression","level":2,"score":0.5961121916770935},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5057721138000488},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4320800304412842},{"id":"https://openalex.org/C78548338","wikidata":"https://www.wikidata.org/wiki/Q2493","display_name":"Data compression","level":2,"score":0.43152570724487305},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.42825746536254883},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.12178170680999756},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11902880668640137},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tvlsi.2018.2808104","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tvlsi.2018.2808104","pdf_url":null,"source":{"id":"https://openalex.org/S37538908","display_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","issn_l":"1063-8210","issn":["1063-8210","1557-9999"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Very Large Scale Integration (VLSI) Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8100000023841858,"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy"}],"awards":[{"id":"https://openalex.org/G1252091851","display_name":null,"funder_award_id":"CCF-1253670","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":65,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1608462934","https://openalex.org/W1686810756","https://openalex.org/W1976818984","https://openalex.org/W1981182985","https://openalex.org/W1985956780","https://openalex.org/W1992825118","https://openalex.org/W1999085092","https://openalex.org/W2000668185","https://openalex.org/W2008967422","https://openalex.org/W2009295221","https://openalex.org/W2020192881","https://openalex.org/W2022427297","https://openalex.org/W2031454541","https://openalex.org/W2031489346","https://openalex.org/W2037759417","https://openalex.org/W2046004779","https://openalex.org/W2046580775","https://openalex.org/W2062130228","https://openalex.org/W2074091263","https://openalex.org/W2075645037","https://openalex.org/W2077513643","https://openalex.org/W2101850393","https://openalex.org/W2104671481","https://openalex.org/W2107775979","https://openalex.org/W2115452265","https://openalex.org/W2119144962","https://openalex.org/W2121955477","https://openalex.org/W2123533187","https://openalex.org/W2125556102","https://openalex.org/W2140093718","https://openalex.org/W2142679357","https://openalex.org/W2151103935","https://openalex.org/W2154740476","https://openalex.org/W2156425544","https://openalex.org/W2156547346","https://openalex.org/W2159386181","https://openalex.org/W2161969291","https://openalex.org/W2162255144","https://openalex.org/W2162741153","https://openalex.org/W2163605009","https://openalex.org/W2166623283","https://openalex.org/W2167215970","https://openalex.org/W2169671170","https://openalex.org/W2170110077","https://openalex.org/W2172166488","https://openalex.org/W2182834618","https://openalex.org/W2257868038","https://openalex.org/W2285660444","https://openalex.org/W2289252105","https://openalex.org/W2507063361","https://openalex.org/W2604406773","https://openalex.org/W2613718673","https://openalex.org/W2620730044","https://openalex.org/W2757644909","https://openalex.org/W2950248853","https://openalex.org/W2963087201","https://openalex.org/W2963893493","https://openalex.org/W2964299589","https://openalex.org/W6620707391","https://openalex.org/W6656118373","https://openalex.org/W6683703535","https://openalex.org/W6684191040","https://openalex.org/W6684563725","https://openalex.org/W6685405536"],"related_works":["https://openalex.org/W2547124190","https://openalex.org/W2385628723","https://openalex.org/W2888954728","https://openalex.org/W2552401318","https://openalex.org/W108076602","https://openalex.org/W3180760233","https://openalex.org/W4384342390","https://openalex.org/W3035703949","https://openalex.org/W4247601675","https://openalex.org/W1033938421"],"abstract_inverted_index":{"Pedestrian":[0,46,192],"detection":[1,35,50,64,77],"represents":[2],"an":[3,153],"important":[4],"application":[5],"for":[6,57,94,156],"embedded":[7],"vision":[8],"systems.":[9],"Focusing":[10],"on":[11,43,189],"the":[12,44,63,101,110,116,149,190],"most":[13],"energy":[14,111,198,203],"constrained":[15],"implementations,":[16],"systems":[17,56],"have":[18],"typically":[19],"employed":[20],"histogram":[21],"of":[22,41,115,148,187],"oriented":[23],"gradients":[24],"features":[25,83],"and":[26,89,112,130,136,201],"support":[27],"vector":[28],"machine":[29],"classification,":[30],"which":[31,60,108,168],"leads":[32],"to":[33,127,133],"low":[34],"accuracy":[36,78],"(a":[37],"log-average":[38,184],"miss":[39,185],"rate":[40,186],"68%":[42],"Caltech":[45,191],"dataset).":[47],"Additionally,":[48],"single-scale":[49],"is":[51,162],"often":[52],"adopted":[53],"in":[54,98,152,164],"these":[55],"real-time":[58],"processing,":[59],"further":[61],"deteriorates":[62],"performance.":[65],"In":[66],"this":[67],"paper,":[68],"we":[69,121,145],"propose":[70],"a":[71,183],"hardware":[72],"accelerator":[73,161],"achieving":[74],"substantially":[75],"higher":[76,99,102],"by":[79,199,204],"employing":[80],"aggregated":[81],"channel":[82],"(ACFs)":[84],"at":[85,182],"multiple":[86],"different":[87],"scales":[88],"using":[90],"boosted":[91],"decision":[92],"trees":[93],"classification.":[95],"Though":[96],"resulting":[97,151],"accuracy,":[100],"dimensionality":[103],"ACFs":[104],"exacerbate":[105],"memory":[106,197],"operations,":[107],"become":[109],"speed":[113],"bottlenecks":[114],"system.":[117],"To":[118],"overcome":[119],"this,":[120],"employ":[122],"binary":[123],"discrete":[124],"cosine":[125],"transform":[126],"perform":[128],"low-overhead":[129],"lossy":[131],"compression,":[132,144],"efficiently":[134],"store":[135],"access":[137],"feature":[138],"data.":[139],"For":[140],"restoring":[141],"performance":[142],"following":[143],"exploit":[146],"retraining":[147],"classifier,":[150],"optimal":[154],"model":[155],"pedestrian":[157],"detection.":[158],"The":[159],"proposed":[160],"implemented":[163],"field-programmable":[165],"gate":[166],"array,":[167],"can":[169],"process":[170],"40":[171],"video":[172],"graphics":[173],"array":[174],"frames":[175],"(640":[176],"\u00d7":[177,206],"480":[178],"resolution)":[179],"per":[180],"second":[181],"42%":[188],"dataset,":[193],"with":[194],"compression":[195],"reducing":[196],"4\u00d7":[200],"overall":[202],"1.7":[205],".":[207]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
