{"id":"https://openalex.org/W4362704833","doi":"https://doi.org/10.26599/bdma.2022.9020040","title":"Human Action Recognition Using Difference of Gaussian and Difference of Wavelet","display_name":"Human Action Recognition Using Difference of Gaussian and Difference of Wavelet","publication_year":2023,"publication_date":"2023-04-07","ids":{"openalex":"https://openalex.org/W4362704833","doi":"https://doi.org/10.26599/bdma.2022.9020040"},"language":"en","primary_location":{"id":"doi:10.26599/bdma.2022.9020040","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2022.9020040","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.26599/bdma.2022.9020040","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068674660","display_name":"Gopampallikar Vinoda Reddy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gopampallikar Vinoda Reddy","raw_affiliation_strings":["CMR Technical Campus,Department of Computer Science and Engineering (AI&ML),Hyderabad,India,501401"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CMR Technical Campus,Department of Computer Science and Engineering (AI&ML),Hyderabad,India,501401","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077220522","display_name":"K. Deepika","orcid":"https://orcid.org/0000-0002-6516-188X"},"institutions":[{"id":"https://openalex.org/I28210344","display_name":"Kakatiya University","ror":"https://ror.org/012zpbg81","country_code":"IN","type":"education","lineage":["https://openalex.org/I28210344"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kongara Deepika","raw_affiliation_strings":["Kakatiya Institute of Technology and Science,Department of Information Technology,Warangal,India,506015"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Kakatiya Institute of Technology and Science,Department of Information Technology,Warangal,India,506015","institution_ids":["https://openalex.org/I28210344"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080320584","display_name":"Lakshmanan Malliga","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lakshmanan Malliga","raw_affiliation_strings":["Malla Reddy Engineering College for Women (Autonomous),Department of Electronics and Communication Engineering,Hyderabad,India,500100"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Malla Reddy Engineering College for Women (Autonomous),Department of Electronics and Communication Engineering,Hyderabad,India,500100","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062195698","display_name":"D. Hemanand","orcid":"https://orcid.org/0000-0002-3693-3350"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duraivelu Hemanand","raw_affiliation_strings":["S. A. Engineering College (Autonomous),Department of Computer Science and Engineering,Thiruverkadu,India,600077"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"S. A. Engineering College (Autonomous),Department of Computer Science and Engineering,Thiruverkadu,India,600077","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074723644","display_name":"C. Senthilkumar","orcid":null},"institutions":[{"id":"https://openalex.org/I85461943","display_name":"Saveetha University","ror":"https://ror.org/0034me914","country_code":"IN","type":"education","lineage":["https://openalex.org/I85461943"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Chinnadurai Senthilkumar","raw_affiliation_strings":["Saveetha Institute of Medical and Technical Sciences (SIMATS),Department of Electronics and Communication Engineering,Chennai,India,602105"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Saveetha Institute of Medical and Technical Sciences (SIMATS),Department of Electronics and Communication Engineering,Chennai,India,602105","institution_ids":["https://openalex.org/I85461943"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Subburayalu Gopalakrishnan","orcid":null},"institutions":[{"id":"https://openalex.org/I1330855593","display_name":"Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology","ror":"https://ror.org/05bc5bx80","country_code":"IN","type":"education","lineage":["https://openalex.org/I1330855593"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Subburayalu Gopalakrishnan","raw_affiliation_strings":["Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Department of Electronics and Communication Engineering,Chennai,India,600062"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Department of Electronics and Communication Engineering,Chennai,India,600062","institution_ids":["https://openalex.org/I1330855593"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028356071","display_name":"Yousef Farhaoui","orcid":"https://orcid.org/0000-0003-0870-6262"},"institutions":[{"id":"https://openalex.org/I4210153608","display_name":"Universit\u00e9 Moulay Ismail de Meknes","ror":"https://ror.org/04cnscd67","country_code":"MA","type":"education","lineage":["https://openalex.org/I4210153608"]}],"countries":["MA"],"is_corresponding":false,"raw_author_name":"Yousef Farhaoui","raw_affiliation_strings":["IDMS Team, STI Laboratory, Faculty of Sciences and Techniques, Moulay Ismail University of Mcknès,Errachidia,Morocco,25003"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IDMS Team, STI Laboratory, Faculty of Sciences and Techniques, Moulay Ismail University of Mcknès,Errachidia,Morocco,25003","institution_ids":["https://openalex.org/I4210153608"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":6.8489,"has_fulltext":false,"cited_by_count":61,"citation_normalized_percentile":{"value":0.97891829,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":100},"biblio":{"volume":"6","issue":"3","first_page":"336","last_page":"346"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9973999857902527,"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/T12740","display_name":"Gait Recognition and Analysis","score":0.9969000220298767,"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/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6989679336547852},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6286062598228455},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.622719943523407},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5353980660438538},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5337969064712524},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.5144641399383545},{"id":"https://openalex.org/keywords/linear-discriminant-analysis","display_name":"Linear discriminant analysis","score":0.49348747730255127},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.46602535247802734},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.421597421169281},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.412703275680542},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4068323075771332},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2545536756515503}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6989679336547852},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6286062598228455},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.622719943523407},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5353980660438538},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5337969064712524},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.5144641399383545},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.49348747730255127},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.46602535247802734},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.421597421169281},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.412703275680542},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4068323075771332},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2545536756515503},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.26599/bdma.2022.9020040","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2022.9020040","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:98695e8ba5fc45c498d34a4733a1fed4","is_oa":true,"landing_page_url":"https://doaj.org/article/98695e8ba5fc45c498d34a4733a1fed4","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Big Data Mining and Analytics, Vol 6, Iss 3, Pp 336-346 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.26599/bdma.2022.9020040","is_oa":true,"landing_page_url":"https://doi.org/10.26599/bdma.2022.9020040","pdf_url":null,"source":{"id":"https://openalex.org/S4210209060","display_name":"Big Data Mining and Analytics","issn_l":"2096-0654","issn":["2096-0654"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310311901","host_organization_name":"Tsinghua University Press","host_organization_lineage":["https://openalex.org/P4310311901"],"host_organization_lineage_names":["Tsinghua University Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Big Data Mining and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5899999737739563,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1718812716","https://openalex.org/W1964166080","https://openalex.org/W1985809919","https://openalex.org/W1991503487","https://openalex.org/W2005746713","https://openalex.org/W2010399676","https://openalex.org/W2100916003","https://openalex.org/W2101323620","https://openalex.org/W2123570766","https://openalex.org/W2533474511","https://openalex.org/W2624721993","https://openalex.org/W2765966441","https://openalex.org/W2790194913","https://openalex.org/W2908643347","https://openalex.org/W3035227524","https://openalex.org/W3118773068"],"related_works":["https://openalex.org/W2794812819","https://openalex.org/W2114217318","https://openalex.org/W2587881214","https://openalex.org/W3104072235","https://openalex.org/W1995622179","https://openalex.org/W2048680804","https://openalex.org/W2370263288","https://openalex.org/W1484111231","https://openalex.org/W2169311637","https://openalex.org/W3192451249"],"abstract_inverted_index":{"Human":[0],"Action":[1],"Recognition":[2],"(HAR)":[3],"attempts":[4],"to":[5,62,73,115,126,137,163,257],"recognize":[6],"the":[7,20,28,89,128,142,146,150,178,203,225,246,250,258,265,268],"human":[8],"action":[9,24,37,87,211],"from":[10],"images":[11],"and":[12,49,66,94,131,145,186,195,233,242],"videos.":[13],"The":[14,52,166,198],"major":[15],"challenge":[16],"in":[17,41,267],"HAR":[18,29],"is":[19,39,112,124,161,173,191,220,238,254],"design":[21],"of":[22,58,69,91,96,141,169,181,183,188,205,228,230,235,252,260,270],"an":[23],"descriptor":[25,38,54],"that":[26],"makes":[27],"system":[30],"robust":[31],"for":[32,83,157,213],"different":[33],"environments.":[34],"A":[35],"novel":[36],"proposed":[40,53,206],"this":[42],"study,":[43],"based":[44],"on":[45,154],"two":[46],"independent":[47],"spatial":[48],"spectral":[50,75],"filters.":[51],"uses":[55],"a":[56,67,79,84,106,120],"Difference":[57,68,95,187,229,234],"Gaussian":[59],"(DoG)":[60,93,185,232],"filter":[61,72],"extract":[63,74],"scale-invariant":[64],"features":[65,99],"Wavelet":[70,97,189,236],"(DoW)":[71,98,190,237],"information.":[76],"To":[77],"create":[78],"composite":[80],"feature":[81],"vector":[82],"particular":[85],"test":[86],"picture,":[88],"Discriminant":[90,103],"Guassian":[92,184,231],"are":[100],"combined.":[101],"Linear":[102],"Analysis":[104],"(LDA),":[105],"widely":[107],"used":[108,114,125,136],"dimensionality":[109],"reduction":[110],"technique,":[111],"also":[113],"eliminate":[116],"duplicate":[117],"data.":[118],"Finally,":[119],"closest":[121],"neighbor":[122],"method":[123],"classify":[127],"dataset.":[129],"Weizmann":[130,155,253],"UCF":[132,209,261],"11":[133,210],"datasets":[134,156],"were":[135,152],"run":[138,153],"extensive":[139],"simulations":[140,151],"suggested":[143],"strategy,":[144],"accuracy":[147,168,180,200,227,248,251,259],"assessed":[148],"after":[149,202],"five-fold":[158,214],"cross":[159,215],"validation":[160,216],"shown":[162],"perform":[164],"well.":[165],"average":[167,179,199,226],"DoG":[170,217],"+":[171,218],"DoW":[172,219],"observed":[174,192,221,239],"as":[175,193,222,240],"83.6635%":[176],"while":[177,224],"Discrinanat":[182],"80.2312%":[194],"77.4215%,":[196],"respectively.":[197,244],"measured":[201],"simulation":[204],"methods":[207],"over":[208],"dataset":[212],"62.5231%":[223],"60.3214%":[241],"58.1247%,":[243],"From":[245],"above":[247],"observations,":[249],"high":[255],"compared":[256],"11,":[262],"hence":[263],"verifying":[264],"effectiveness":[266],"improvisation":[269],"recognition":[271],"accuracy.":[272]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":27},{"year":2024,"cited_by_count":30},{"year":2023,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}