{"id":"https://openalex.org/W3138046378","doi":"https://doi.org/10.1109/bigdata50022.2020.9377733","title":"Action Recognition Using Dynamic Mode Decomposition for Temporal Representation","display_name":"Action Recognition Using Dynamic Mode Decomposition for Temporal Representation","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3138046378","doi":"https://doi.org/10.1109/bigdata50022.2020.9377733","mag":"3138046378"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9377733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5002704589","display_name":"Kyle Pawlowski","orcid":null},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Kyle Pawlowski","raw_affiliation_strings":["Electrical Engineering, Kennesaw State University, Kennesaw, GA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering, Kennesaw State University, Kennesaw, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058716630","display_name":"Sumit Chakravarty","orcid":"https://orcid.org/0000-0001-8108-8726"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sumit Chakravarty","raw_affiliation_strings":["Electrical Engineering, Kennesaw State University, Kennesaw, GA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical Engineering, Kennesaw State University, Kennesaw, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101628906","display_name":"Ying Xie","orcid":"https://orcid.org/0000-0002-6419-3986"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Xie","raw_affiliation_strings":["Software Engineering, Kennesaw State University, Kennesaw, GA, USA"],"affiliations":[{"raw_affiliation_string":"Software Engineering, Kennesaw State University, Kennesaw, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084716943","display_name":"Arjun Kumar Joginipelly","orcid":"https://orcid.org/0000-0002-0821-6127"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arjun Kumar Joginipelly","raw_affiliation_strings":["Freedom Electronics LLC, Kennesaw, GA, USA"],"affiliations":[{"raw_affiliation_string":"Freedom Electronics LLC, Kennesaw, GA, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5002704589"],"corresponding_institution_ids":["https://openalex.org/I172980758"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44397392,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"5381","last_page":"5386"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9987999796867371,"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":0.9987999796867371,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.998199999332428,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9567000269889832,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.8077514171600342},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.718915581703186},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6220961809158325},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6183810234069824},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5891686677932739},{"id":"https://openalex.org/keywords/dynamic-mode-decomposition","display_name":"Dynamic mode decomposition","score":0.5823036432266235},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5427286028862},{"id":"https://openalex.org/keywords/decomposition","display_name":"Decomposition","score":0.5404585003852844},{"id":"https://openalex.org/keywords/temporal-database","display_name":"Temporal database","score":0.5397949814796448},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.43570995330810547},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.43256473541259766},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.4153895080089569},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.410036563873291},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3726641535758972},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2918633222579956}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8077514171600342},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.718915581703186},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6220961809158325},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6183810234069824},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5891686677932739},{"id":"https://openalex.org/C2777032711","wikidata":"https://www.wikidata.org/wiki/Q5318993","display_name":"Dynamic mode decomposition","level":2,"score":0.5823036432266235},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5427286028862},{"id":"https://openalex.org/C124681953","wikidata":"https://www.wikidata.org/wiki/Q339062","display_name":"Decomposition","level":2,"score":0.5404585003852844},{"id":"https://openalex.org/C77277458","wikidata":"https://www.wikidata.org/wiki/Q1969246","display_name":"Temporal database","level":2,"score":0.5397949814796448},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.43570995330810547},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.43256473541259766},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4153895080089569},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.410036563873291},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3726641535758972},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2918633222579956},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9377733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W2014356541","https://openalex.org/W2117130368","https://openalex.org/W2126579184","https://openalex.org/W2156303437","https://openalex.org/W2341174028","https://openalex.org/W2399643973","https://openalex.org/W2508429489","https://openalex.org/W2785514482","https://openalex.org/W2796347433","https://openalex.org/W2963119563","https://openalex.org/W2963173190","https://openalex.org/W2963524571","https://openalex.org/W4238160257","https://openalex.org/W4293584584","https://openalex.org/W6600983433","https://openalex.org/W6682864246","https://openalex.org/W6703873297","https://openalex.org/W6750227808"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2761785940","https://openalex.org/W2597763219","https://openalex.org/W2014356541","https://openalex.org/W2182357018"],"abstract_inverted_index":{"This":[0,45,86],"paper":[1],"explores":[2],"a":[3,17,58],"new":[4],"method":[5,18,87],"for":[6,26],"representing":[7],"temporal":[8,80],"information":[9],"found":[10],"in":[11,36],"videos.":[12],"Dynamic":[13],"Mode":[14],"Decomposition":[15],"(DMD),":[16],"commonly":[19],"used":[20,35],"to":[21,39,48],"reduce":[22],"the":[23,49,72,76,79,96,100,107,112],"computational":[24],"effort":[25],"other":[27,77],"big-data":[28],"tasks":[29,56],"such":[30],"as":[31,82],"flow":[32],"calculations,":[33],"is":[34,46,88],"this":[37],"study":[38],"aggregate":[40],"changes":[41],"between":[42],"multiple":[43],"frames.":[44],"applied":[47],"challenge":[50],"of":[51],"human":[52],"action":[53],"recognition":[54],"(HAR)":[55],"using":[57,84,92],"Two-Stream":[59],"architecture.":[60],"Such":[61],"an":[62],"architecture":[63],"takes":[64],"two":[65,93],"convolutional":[66],"neural":[67],"networks":[68],"(CNNs),":[69],"one":[70],"analyzing":[71,78],"spatial":[73],"data":[74,81],"and":[75,99,109],"calculated":[83],"DMD.":[85],"compared":[89],"against":[90],"others":[91],"common":[94],"benchmarks,":[95],"UCF-101":[97,108],"dataset":[98],"HMDB-51":[101],"dataset,":[102],"achieving":[103],"46.0%":[104],"accuracy":[105],"on":[106,111],"30.8%":[110],"HMDB-51.":[113]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}