{"id":"https://openalex.org/W2181312074","doi":"https://doi.org/10.1109/sips.2015.7345020","title":"A speed up efficient match kernel for visual estimation and detection","display_name":"A speed up efficient match kernel for visual estimation and detection","publication_year":2015,"publication_date":"2015-10-01","ids":{"openalex":"https://openalex.org/W2181312074","doi":"https://doi.org/10.1109/sips.2015.7345020","mag":"2181312074"},"language":"en","primary_location":{"id":"doi:10.1109/sips.2015.7345020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips.2015.7345020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Workshop on Signal Processing Systems (SiPS)","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":null,"display_name":"Han Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Han Hong","raw_affiliation_strings":["School of Electronic Engineering, Xidian University, Xi'an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015433638","display_name":"Minglei Tong","orcid":"https://orcid.org/0000-0003-2589-5755"},"institutions":[{"id":"https://openalex.org/I23632641","display_name":"Shanghai University of Electric Power","ror":"https://ror.org/02w4tny03","country_code":"CN","type":"education","lineage":["https://openalex.org/I23632641"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minglei Tong","raw_affiliation_strings":["School of Electronic and Information, Shanghai University of Electronic Power, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic and Information, Shanghai University of Electronic Power, Shanghai, China","institution_ids":["https://openalex.org/I23632641"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1191695,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"4","issue":null,"first_page":"1","last_page":"6"},"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":0.9998000264167786,"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":0.9998000264167786,"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":0.9995999932289124,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9984999895095825,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8155925273895264},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7482705116271973},{"id":"https://openalex.org/keywords/pose","display_name":"Pose","score":0.715259313583374},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6062701344490051},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5812712907791138},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5777236223220825},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5684003829956055},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5395469069480896},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5147919654846191},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5096256732940674},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.4850136935710907},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4785783290863037},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4534112215042114},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.45162126421928406},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4303743541240692},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4195631742477417},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4131605625152588},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.412034273147583},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17328330874443054},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.11859369277954102},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.08416593074798584}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8155925273895264},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7482705116271973},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.715259313583374},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6062701344490051},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5812712907791138},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5777236223220825},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5684003829956055},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5395469069480896},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5147919654846191},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5096256732940674},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.4850136935710907},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4785783290863037},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4534112215042114},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.45162126421928406},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4303743541240692},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4195631742477417},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4131605625152588},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.412034273147583},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17328330874443054},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.11859369277954102},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.08416593074798584},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/sips.2015.7345020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/sips.2015.7345020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Workshop on Signal Processing Systems (SiPS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"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":43,"referenced_works":["https://openalex.org/W1571870753","https://openalex.org/W1589844095","https://openalex.org/W1677409904","https://openalex.org/W2030536784","https://openalex.org/W2032682867","https://openalex.org/W2096112371","https://openalex.org/W2098770944","https://openalex.org/W2099333815","https://openalex.org/W2101150485","https://openalex.org/W2104978738","https://openalex.org/W2106159958","https://openalex.org/W2111308925","https://openalex.org/W2117138194","https://openalex.org/W2119823327","https://openalex.org/W2123503110","https://openalex.org/W2128638437","https://openalex.org/W2131846894","https://openalex.org/W2134692386","https://openalex.org/W2134717813","https://openalex.org/W2140095548","https://openalex.org/W2142422094","https://openalex.org/W2151103935","https://openalex.org/W2151259137","https://openalex.org/W2154707797","https://openalex.org/W2156971099","https://openalex.org/W2157041524","https://openalex.org/W2161969291","https://openalex.org/W2162880125","https://openalex.org/W2162915993","https://openalex.org/W2169738563","https://openalex.org/W2186233030","https://openalex.org/W2435338979","https://openalex.org/W3005890292","https://openalex.org/W6635090938","https://openalex.org/W6674636832","https://openalex.org/W6675086664","https://openalex.org/W6675273677","https://openalex.org/W6675782445","https://openalex.org/W6677833576","https://openalex.org/W6680011651","https://openalex.org/W6680210611","https://openalex.org/W6683120118","https://openalex.org/W6718488237"],"related_works":["https://openalex.org/W2802018156","https://openalex.org/W4307623796","https://openalex.org/W4313315626","https://openalex.org/W2101531944","https://openalex.org/W2922437833","https://openalex.org/W2100052226","https://openalex.org/W4312696271","https://openalex.org/W4223892596","https://openalex.org/W2933098581","https://openalex.org/W2556125083"],"abstract_inverted_index":{"Low":[0],"level":[1,104],"local":[2],"descriptors":[3],"are":[4],"so":[5],"successful":[6],"for":[7,111,132,174],"their":[8,26],"excellent":[9],"performance":[10],"in":[11,117,155,178],"image":[12,65,105,124,129],"correspondence":[13],"matching":[14],"problems.":[15],"However,":[16],"due":[17],"to":[18,31,68,76,98,127],"the":[19,80,101,109,112],"intrinsic":[20],"nature":[21],"of":[22,28],"high":[23],"computational":[24],"complexity,":[25],"areas":[27],"application":[29],"shrink":[30],"a":[32,48,54],"limited":[33],"ground.":[34],"In":[35],"this":[36],"paper,":[37],"we":[38,95],"address":[39],"visual":[40,69,180],"pedestrian":[41],"detection":[42,70,156],"and":[43,71,75,136,151,157,172],"human":[44,72,133,158],"pose":[45,73,115,159],"estimation":[46,88,160],"with":[47,87,91,144],"learning":[49,81],"based":[50],"feature":[51,66,177],"representation":[52,106],"from":[53,123],"speed":[55],"up":[56],"efficient":[57],"match":[58],"kernel,":[59],"by":[60,121],"mainly":[61],"focusing":[62],"on":[63,162],"what":[64],"contributes":[67],"estimation,":[74],"how":[77],"much":[78],"extent":[79],"process":[82],"actually":[83],"matters.":[84],"When":[85],"dealing":[86],"accuracy":[89,110],"combined":[90],"state-of-art":[92],"prediction":[93,116],"methods,":[94],"would":[96],"like":[97],"testify":[99],"that":[100],"modified":[102],"patch":[103],"could":[107],"boost":[108],"final":[113],"3D":[114],"monocular":[118],"video":[119],"images":[120],"information":[122],"structural":[125],"correlation":[126],"characterize":[128],"context,":[130],"especially":[131],"body":[134],"shapes":[135],"motions.":[137],"We":[138],"have":[139],"compared":[140],"our":[141,175],"proposed":[142,176],"method":[143],"classic":[145],"global":[146],"features":[147],"such":[148,179],"as":[149],"HOG,":[150],"conducted":[152],"comprehensive":[153],"experiments":[154],"tasks":[161],"benchmark":[163],"datasets.":[164],"Final":[165],"evaluation":[166],"result":[167],"verifies":[168],"competitive":[169],"discriminatory":[170],"effectiveness":[171],"distinctiveness":[173],"tasks.":[181]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}