{"id":"https://openalex.org/W2795597953","doi":"https://doi.org/10.1109/lsp.2018.2823910","title":"Global Temporal Representation Based CNNs for Infrared Action Recognition","display_name":"Global Temporal Representation Based CNNs for Infrared Action Recognition","publication_year":2018,"publication_date":"2018-04-06","ids":{"openalex":"https://openalex.org/W2795597953","doi":"https://doi.org/10.1109/lsp.2018.2823910","mag":"2795597953"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2018.2823910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2018.2823910","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","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/A5100356036","display_name":"Yang Liu","orcid":"https://orcid.org/0000-0002-9423-9252"},"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":true,"raw_author_name":"Yang Liu","raw_affiliation_strings":["School of Telecommunications Engineering, Xidian University, Xi\u2019an, China","School of Telecommunications Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015936673","display_name":"Zhaoyang Lu","orcid":"https://orcid.org/0000-0003-0949-6981"},"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":"Zhaoyang Lu","raw_affiliation_strings":["School of Telecommunications Engineering, Xidian University, Xi\u2019an, China","School of Telecommunications Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100763416","display_name":"Jing Li","orcid":"https://orcid.org/0000-0002-9043-8633"},"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":"Jing Li","raw_affiliation_strings":["School of Telecommunications Engineering, Xidian University, Xi\u2019an, China","School of Telecommunications Engineering, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"School of Telecommunications Engineering, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062706107","display_name":"Tao Yang","orcid":"https://orcid.org/0000-0002-5180-2316"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Yang","raw_affiliation_strings":["SAIIP, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an, China","SAIIP, School of Computer Science, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"SAIIP, School of Computer Science, Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"SAIIP, School of Computer Science, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047997849","display_name":"Chao Yao","orcid":"https://orcid.org/0000-0003-0988-6349"},"institutions":[{"id":"https://openalex.org/I17145004","display_name":"Northwestern Polytechnical University","ror":"https://ror.org/01y0j0j86","country_code":"CN","type":"education","lineage":["https://openalex.org/I17145004"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Yao","raw_affiliation_strings":["School of Automation, Northwestern Polytechnical University, Xi\u2019an, China","School of Automation, Northwestern Polytechnical University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"School of Automation, Northwestern Polytechnical University, Xi\u2019an, China","institution_ids":["https://openalex.org/I17145004"]},{"raw_affiliation_string":"School of Automation, Northwestern Polytechnical University, Xi'an, China","institution_ids":["https://openalex.org/I17145004"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100356036"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":2.507,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":{"value":0.92345765,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"25","issue":"6","first_page":"848","last_page":"852"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":1.0,"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":1.0,"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":0.9987000226974487,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9972000122070312,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7695302963256836},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.742936372756958},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7267847657203674},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6466371417045593},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5488340854644775},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.4979746341705322},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.48070961236953735},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4769269824028015},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.4333258867263794},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4297635555267334},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.42043808102607727}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7695302963256836},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.742936372756958},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7267847657203674},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6466371417045593},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5488340854644775},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.4979746341705322},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.48070961236953735},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4769269824028015},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.4333258867263794},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4297635555267334},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.42043808102607727},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lsp.2018.2823910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2018.2823910","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"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 Signal Processing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7300000190734863,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G1679109303","display_name":null,"funder_award_id":"61672429","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2108256826","display_name":null,"funder_award_id":"61272288","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4196744919","display_name":null,"funder_award_id":"61502364","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6378183406","display_name":null,"funder_award_id":"3102016ZY022","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W1944615693","https://openalex.org/W1981419085","https://openalex.org/W1983364832","https://openalex.org/W2027922120","https://openalex.org/W2035669656","https://openalex.org/W2062118960","https://openalex.org/W2068611653","https://openalex.org/W2105101328","https://openalex.org/W2153635508","https://openalex.org/W2155893237","https://openalex.org/W2156303437","https://openalex.org/W2235034809","https://openalex.org/W2253590344","https://openalex.org/W2342662179","https://openalex.org/W2397183714","https://openalex.org/W2462996230","https://openalex.org/W2473890477","https://openalex.org/W2522485430","https://openalex.org/W2600088275","https://openalex.org/W2604690669","https://openalex.org/W2614128781","https://openalex.org/W2623040213","https://openalex.org/W2761860076","https://openalex.org/W2763446669","https://openalex.org/W2787943737","https://openalex.org/W2963119563","https://openalex.org/W2964134613","https://openalex.org/W3099128265","https://openalex.org/W3103858256","https://openalex.org/W6600983433","https://openalex.org/W6682864246"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W3103989898","https://openalex.org/W2004108207","https://openalex.org/W2182357018"],"abstract_inverted_index":{"Infrared":[0],"human":[1],"action":[2,22,79,99,179,190,220],"recognition":[3,23,191],"has":[4],"many":[5],"advantages,":[6],"i.e.,":[7],"it":[8],"is":[9,51,174],"insensitive":[10],"to":[11,92,115,176],"illumination":[12],"change,":[13],"appearance":[14],"variability,":[15],"and":[16,70,73,86,110,194,211],"shadows.":[17],"Existing":[18],"methods":[19,216],"for":[20,217],"infrared":[21,78,98,189,219],"are":[24,136],"either":[25],"based":[26,215],"on":[27,107,188],"spatial":[28,87],"or":[29],"local":[30],"temporal":[31,36,62,88,112],"information,":[32,37],"however,":[33],"the":[34,42,48,77,83,93,97,122,152,159,178,186,202,206,218],"global":[35,61,111],"which":[38],"can":[39],"better":[40],"describe":[41],"movements":[43],"of":[44,96,154],"body":[45],"parts":[46],"across":[47],"whole":[49],"video,":[50],"not":[52],"considered.":[53],"In":[54],"this":[55],"letter,":[56],"we":[57,101,150],"propose":[58],"a":[59,128,168],"novel":[60],"representation":[63],"named":[64,141],"optical-flow":[65],"stacked":[66],"difference":[67],"image":[68],"(OFSDI)":[69],"extract":[71],"robust":[72],"discriminative":[74],"feature":[75,119,134],"from":[76,121],"data":[80,124,180],"by":[81,146,157],"considering":[82],"local,":[84,108],"global,":[85],"information":[89],"together.":[90],"Due":[91],"small":[94],"size":[95],"dataset,":[100],"first":[102],"apply":[103],"convolutional":[104,118,133],"neural":[105],"networks":[106],"spatial,":[109],"stream":[113],"respectively":[114],"obtain":[116],"efficient":[117],"maps":[120,135],"raw":[123],"rather":[125],"than":[126],"train":[127],"classifier":[129],"directly.":[130],"Then":[131],"these":[132,155,166],"aggregated":[137],"into":[138],"effective":[139],"descriptors":[140,145],"three-stream":[142],"trajectory-pooled":[143],"deep-convolutional":[144],"trajectory-constrained":[147],"pooling.":[148],"Furthermore,":[149],"improve":[151],"robustness":[153],"features":[156,210,214],"using":[158],"locality-constrained":[160],"linear":[161,169],"coding":[162],"(LLC)":[163],"method.":[164],"With":[165],"features,":[167],"support":[170],"vector":[171],"machine":[172],"(SVM)":[173],"adopted":[175],"classify":[177],"in":[181],"our":[182],"scheme.":[183],"We":[184],"conduct":[185],"experiments":[187],"datasets":[192],"InfAR":[193],"NTU":[195],"RGB+D.":[196],"The":[197],"experimental":[198],"results":[199],"show":[200],"that":[201],"proposed":[203],"approach":[204],"outperforms":[205],"representative":[207],"state-of-the-art":[208],"handcrafted":[209],"deep":[212],"learning":[213],"recognition.":[221]},"counts_by_year":[{"year":2025,"cited_by_count":10},{"year":2024,"cited_by_count":9},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":14},{"year":2020,"cited_by_count":5},{"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"}