{"id":"https://openalex.org/W3004038674","doi":"https://doi.org/10.1117/12.2559432","title":"Multi-channels CNN temporal features for depth-based action recognition","display_name":"Multi-channels CNN temporal features for depth-based action recognition","publication_year":2020,"publication_date":"2020-01-31","ids":{"openalex":"https://openalex.org/W3004038674","doi":"https://doi.org/10.1117/12.2559432","mag":"3004038674"},"language":"en","primary_location":{"id":"doi:10.1117/12.2559432","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2559432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Twelfth International Conference on Machine Vision (ICMV 2019)","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/A5009962591","display_name":"Jacek Treli\u0144ski","orcid":null},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Jacek Trelinski","raw_affiliation_strings":["AGH Univ. of Science and Technology (Poland)"],"affiliations":[{"raw_affiliation_string":"AGH Univ. of Science and Technology (Poland)","institution_ids":["https://openalex.org/I686019"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091722048","display_name":"Bogdan Kwolek","orcid":"https://orcid.org/0000-0002-7715-1435"},"institutions":[{"id":"https://openalex.org/I686019","display_name":"AGH University of Krakow","ror":"https://ror.org/00bas1c41","country_code":"PL","type":"education","lineage":["https://openalex.org/I686019"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Bogdan Kwolek","raw_affiliation_strings":["AGH Univ. of Science and Technology (Poland)"],"affiliations":[{"raw_affiliation_string":"AGH Univ. of Science and Technology (Poland)","institution_ids":["https://openalex.org/I686019"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5009962591"],"corresponding_institution_ids":["https://openalex.org/I686019"],"apc_list":null,"apc_paid":null,"fwci":0.0977,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.35635545,"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":"98","last_page":"98"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9707000255584717,"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":0.9550999999046326,"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":0.7427279949188232},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7417014241218567},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6982227563858032},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.6142177581787109},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5925238132476807},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5616332292556763},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5132474899291992},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4868425726890564},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.4108128547668457},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.32239991426467896}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7427279949188232},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7417014241218567},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6982227563858032},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.6142177581787109},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5925238132476807},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5616332292556763},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5132474899291992},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4868425726890564},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.4108128547668457},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.32239991426467896},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2559432","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2559432","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Twelfth International Conference on Machine Vision (ICMV 2019)","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":0,"referenced_works":[],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4321487865","https://openalex.org/W4313906399","https://openalex.org/W4391266461","https://openalex.org/W2590798552","https://openalex.org/W2811106690","https://openalex.org/W4239306820","https://openalex.org/W2947043951","https://openalex.org/W4399188509","https://openalex.org/W2551337514"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,37,48,61,81,101,133],"investigate":[4],"temporal":[5,56],"features":[6,47,57,64,69,92,104],"that":[7,163],"are":[8,70],"extracted":[9],"by":[10,129,151],"a":[11,50,73,83,135,152],"multi-channel":[12,52],"convolutional":[13,86],"neural":[14,87],"network":[15,88],"in":[16,33,72,120],"depth":[17,35,94],"map-based":[18],"human":[19],"action":[20,122,127,130,148],"recognition.":[21],"At":[22],"the":[23,26,30,115,147,167],"beginning,":[24],"for":[25,78],"non-zero":[27],"pixels":[28],"representing":[29],"person":[31],"shape":[32],"each":[34,79,97,108,126],"map":[36],"calculate":[38,102],"handcrafted":[39,46],"features.":[40],"On":[41],"multivariate":[42,99],"time-series":[43,100],"of":[44,65,93,105,141,146],"such":[45,157],"train":[49,82,134],"multi-class,":[51],"CNN":[53],"to":[54,89],"model":[55],"as":[58,60],"well":[59],"extract":[62,90],"statistical":[63,103],"time-series.":[66,106],"The":[67,144],"concatenated":[68,113],"stored":[71],"common":[74,116],"feature":[75,110,117,123,131],"vector.":[76,124],"Afterwards,":[77],"class":[80],"separate":[84],"one-against-all":[85],"class-specific":[91,109],"maps.":[95],"For":[96,125],"class-specific,":[98],"Finally,":[107],"vector":[111,118],"is":[112,149],"with":[114,138],"resulting":[119],"an":[121],"represented":[128],"vectors":[132],"multi-class":[136],"classifier":[137],"one-hot":[139,158],"encoding":[140],"output":[142],"labels.":[143],"recognition":[145],"done":[150],"voting-based":[153],"ensemble":[154],"operating":[155],"on":[156,164,178],"encodings.":[159],"We":[160],"demonstrate":[161],"experimentally":[162],"UTD-MHAD":[165],"dataset":[166],"proposed":[168],"algorithm":[169],"outperforms":[170],"state-of-the-art":[171],"depth-based":[172],"algorithms":[173],"and":[174],"attains":[175],"promising":[176],"results":[177],"MSR-Action3D":[179],"dataset.":[180]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}