{"id":"https://openalex.org/W3002648974","doi":"https://doi.org/10.1109/vcip47243.2019.8965816","title":"Multi-path Convolutional Neural Network based on Rectangular Kernel with Path Signature Features for Gesture Recognition","display_name":"Multi-path Convolutional Neural Network based on Rectangular Kernel with Path Signature Features for Gesture Recognition","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3002648974","doi":"https://doi.org/10.1109/vcip47243.2019.8965816","mag":"3002648974"},"language":"en","primary_location":{"id":"doi:10.1109/vcip47243.2019.8965816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip47243.2019.8965816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Visual Communications and Image Processing (VCIP)","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/A5039681592","display_name":"Lufan Liao","orcid":null},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lufan Liao","raw_affiliation_strings":["South China University of Technology,School of Electronic and Information Engineering","School of Electronic and Information Engineering, South China University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, South China University of Technology","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100327574","display_name":"Xin Zhang","orcid":"https://orcid.org/0000-0003-1583-6401"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Zhang","raw_affiliation_strings":["South China University of Technology,School of Electronic and Information Engineering","School of Electronic and Information Engineering, South China University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, South China University of Technology","institution_ids":["https://openalex.org/I90610280"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100436373","display_name":"Chenyang Li","orcid":"https://orcid.org/0000-0001-6473-5784"},"institutions":[{"id":"https://openalex.org/I90610280","display_name":"South China University of Technology","ror":"https://ror.org/0530pts50","country_code":"CN","type":"education","lineage":["https://openalex.org/I90610280"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyang Li","raw_affiliation_strings":["South China University of Technology,School of Electronic and Information Engineering","School of Electronic and Information Engineering, South China University of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"South China University of Technology,School of Electronic and Information Engineering","institution_ids":["https://openalex.org/I90610280"]},{"raw_affiliation_string":"School of Electronic and Information Engineering, South China University of Technology","institution_ids":["https://openalex.org/I90610280"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I90610280"],"apc_list":null,"apc_paid":null,"fwci":0.3047,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62667695,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","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/T10812","display_name":"Human Pose and Action Recognition","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/T11398","display_name":"Hand Gesture Recognition Systems","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9922000169754028,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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":0.7483675479888916},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7193039059638977},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.683089017868042},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6755574941635132},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.666693925857544},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6528574228286743},{"id":"https://openalex.org/keywords/gesture","display_name":"Gesture","score":0.6459388732910156},{"id":"https://openalex.org/keywords/gesture-recognition","display_name":"Gesture recognition","score":0.5974175333976746},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.5344623327255249},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5049415230751038},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.48696598410606384},{"id":"https://openalex.org/keywords/signature","display_name":"Signature (topology)","score":0.4352879524230957},{"id":"https://openalex.org/keywords/skeleton","display_name":"Skeleton (computer programming)","score":0.42634397745132446},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.4167934060096741},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11142405867576599}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7483675479888916},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7193039059638977},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.683089017868042},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6755574941635132},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.666693925857544},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6528574228286743},{"id":"https://openalex.org/C207347870","wikidata":"https://www.wikidata.org/wiki/Q371174","display_name":"Gesture","level":2,"score":0.6459388732910156},{"id":"https://openalex.org/C159437735","wikidata":"https://www.wikidata.org/wiki/Q1519524","display_name":"Gesture recognition","level":3,"score":0.5974175333976746},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5344623327255249},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5049415230751038},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.48696598410606384},{"id":"https://openalex.org/C2779696439","wikidata":"https://www.wikidata.org/wiki/Q7512811","display_name":"Signature (topology)","level":2,"score":0.4352879524230957},{"id":"https://openalex.org/C18969341","wikidata":"https://www.wikidata.org/wiki/Q1169129","display_name":"Skeleton (computer programming)","level":2,"score":0.42634397745132446},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.4167934060096741},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11142405867576599},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/vcip47243.2019.8965816","is_oa":false,"landing_page_url":"https://doi.org/10.1109/vcip47243.2019.8965816","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Visual Communications and Image Processing (VCIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6899999976158142}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1462226831","https://openalex.org/W1522734439","https://openalex.org/W1966404902","https://openalex.org/W1995113806","https://openalex.org/W2004095444","https://openalex.org/W2108036388","https://openalex.org/W2415469094","https://openalex.org/W2475715656","https://openalex.org/W2606294640","https://openalex.org/W2735590100","https://openalex.org/W2770472008","https://openalex.org/W2771248633","https://openalex.org/W2798498022","https://openalex.org/W2806985529","https://openalex.org/W2962961015","https://openalex.org/W2963370140","https://openalex.org/W2964304707","https://openalex.org/W3106415951","https://openalex.org/W3146797348","https://openalex.org/W6628451283","https://openalex.org/W6716212831","https://openalex.org/W6741265348"],"related_works":["https://openalex.org/W2167155152","https://openalex.org/W2028966255","https://openalex.org/W2077377051","https://openalex.org/W2029299808","https://openalex.org/W2079987883","https://openalex.org/W2242211636","https://openalex.org/W2169849734","https://openalex.org/W2004513049","https://openalex.org/W2016803373","https://openalex.org/W1994032303"],"abstract_inverted_index":{"Skeleton":[0],"based":[1],"gesture":[2,87,141],"recognition":[3],"has":[4],"gained":[5],"more":[6,119],"attention":[7,26],"due":[8],"to":[9,27,59,78,97,125],"its":[10,49],"wide":[11],"application":[12],"and":[13,76,148,150],"large-scale":[14],"databases":[15],"availability.":[16],"Recent":[17],"methods":[18],"designed":[19,55],"for":[20,56,86],"skeleton":[21,36,43,79],"sequence":[22,80],"data":[23,61],"mainly":[24],"pay":[25],"network":[28],"architecture":[29],"but":[30],"ignore":[31],"an":[32],"essential":[33],"characteristic":[34],"of":[35,42,102],"sequences":[37,44],"that":[38],"the":[39,73,152],"temporal":[40,99],"dimensionality":[41],"is":[45],"usually":[46],"higher":[47],"than":[48],"spatial":[50],"dimensionality.":[51],"Directly":[52],"applying":[53],"CNNs":[54],"image":[57],"classification":[58],"skeleton-based":[60],"can":[62],"not":[63],"capture":[64],"this":[65,69],"unique":[66],"property.":[67],"Considering":[68],"fact,":[70],"we":[71,91,121],"propose":[72],"rectangular":[74],"convolution":[75],"pooling":[77],"data.":[81],"Temporal":[82],"features":[83,95,128],"are":[84],"crucial":[85],"action":[88],"recognition.":[89],"Further,":[90],"introduce":[92],"path":[93],"signature":[94],"(PSF)":[96],"represent":[98],"variation":[100],"characteristics":[101],"each":[103],"joint.":[104],"Moreover,":[105],"there":[106],"only":[107],"exist":[108],"a":[109],"few":[110],"minor":[111],"distinctions":[112],"between":[113],"some":[114],"gestures.":[115],"To":[116],"classify":[117],"them":[118],"accurately,":[120],"add":[122],"two":[123,130],"sub-networks":[124],"extract":[126],"discriminative":[127],"from":[129],"hands":[131],"respectively.":[132],"We":[133],"evaluate":[134],"our":[135],"method":[136],"on":[137],"three":[138],"major":[139],"benchmark":[140],"datasets,":[142],"i.e.,":[143],"ChaLearn":[144,146],"2013,":[145],"2016":[147],"MSRC-12,":[149],"reach":[151],"state-of-the-art":[153],"performance.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":3}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}
