{"id":"https://openalex.org/W2647925446","doi":"https://doi.org/10.1109/icassp.2017.7952447","title":"Online action detection and forecast via Multitask deep Recurrent Neural Networks","display_name":"Online action detection and forecast via Multitask deep Recurrent Neural Networks","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2647925446","doi":"https://doi.org/10.1109/icassp.2017.7952447","mag":"2647925446"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7952447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5100761545","display_name":"Chunhui Liu","orcid":"https://orcid.org/0000-0002-9397-7042"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunhui Liu","raw_affiliation_strings":["Institiude of Computer Science and Technology, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institiude of Computer Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029760000","display_name":"Yanghao Li","orcid":"https://orcid.org/0000-0002-5274-1367"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanghao Li","raw_affiliation_strings":["Institiude of Computer Science and Technology, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institiude of Computer Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087570320","display_name":"Yueyu Hu","orcid":"https://orcid.org/0000-0003-4919-4515"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yueyu Hu","raw_affiliation_strings":["Institiude of Computer Science and Technology, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institiude of Computer Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100761525","display_name":"Jiaying Liu","orcid":"https://orcid.org/0000-0002-0468-9576"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaying Liu","raw_affiliation_strings":["Institiude of Computer Science and Technology, Peking University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institiude of Computer Science and Technology, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100761545"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.8192,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.82106159,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1702","last_page":"1706"},"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":0.9975000023841858,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9968000054359436,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/subnetwork","display_name":"Subnetwork","score":0.9487624168395996},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7906608581542969},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.701423168182373},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6136090159416199},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5906931757926941},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5681469440460205},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.540904700756073},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5317161083221436},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5014104843139648},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49906349182128906},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.438967227935791},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39708763360977173},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3309028744697571},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07100814580917358},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06964379549026489}],"concepts":[{"id":"https://openalex.org/C2780186347","wikidata":"https://www.wikidata.org/wiki/Q11414","display_name":"Subnetwork","level":2,"score":0.9487624168395996},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7906608581542969},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.701423168182373},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6136090159416199},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5906931757926941},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5681469440460205},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.540904700756073},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5317161083221436},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5014104843139648},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49906349182128906},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.438967227935791},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39708763360977173},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3309028744697571},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07100814580917358},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06964379549026489},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2017.7952447","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952447","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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":25,"referenced_works":["https://openalex.org/W1606858007","https://openalex.org/W1744759976","https://openalex.org/W1923404803","https://openalex.org/W1947481528","https://openalex.org/W1950788856","https://openalex.org/W2016208906","https://openalex.org/W2016776918","https://openalex.org/W2018068650","https://openalex.org/W2031489346","https://openalex.org/W2031765333","https://openalex.org/W2099634219","https://openalex.org/W2105101328","https://openalex.org/W2130103520","https://openalex.org/W2156303437","https://openalex.org/W2307035320","https://openalex.org/W2341313195","https://openalex.org/W2342662179","https://openalex.org/W2472970127","https://openalex.org/W2952587893","https://openalex.org/W6636412649","https://openalex.org/W6640754710","https://openalex.org/W6679388247","https://openalex.org/W6682864246","https://openalex.org/W6698200468","https://openalex.org/W6704032731"],"related_works":["https://openalex.org/W2060724872","https://openalex.org/W2082094785","https://openalex.org/W2202198356","https://openalex.org/W3087203342","https://openalex.org/W2377184161","https://openalex.org/W228984114","https://openalex.org/W4226360758","https://openalex.org/W2090026684","https://openalex.org/W2907567977","https://openalex.org/W3008584592"],"abstract_inverted_index":{"Online":[0],"human":[1],"action":[2,18,34,128,157],"detection":[3,54,158],"and":[4,20,31,79,93,109,133,159],"forecast":[5,104],"on":[6,16,155],"untrimmed":[7],"3D":[8],"skeleton":[9],"sequences":[10],"is":[11,28,74],"a":[12,36,66,86,118,139],"novel":[13],"task":[14,42],"based":[15],"traditional":[17],"recognition":[19],"has":[21],"not":[22],"been":[23],"fully":[24],"studied.":[25],"Its":[26],"aim":[27],"to":[29,61,89,121,130],"localize":[30],"recognize":[32],"one":[33,91],"in":[35],"long":[37],"sequence":[38],"while":[39],"doing":[40],"forecasting":[41],"at":[43,99],"the":[44,95,100,105,111,127,135,149],"same":[45,101],"time.":[46,102],"In":[47],"this":[48,63],"paper,":[49],"we":[50,84,116,125],"propose":[51],"an":[52],"online":[53,156],"algorithm":[55],"featuring":[56],"Multi-Task":[57],"Recurrent":[58],"Neural":[59],"Network":[60],"solve":[62],"problem.":[64],"First,":[65],"deep":[67],"Long":[68],"Short":[69],"Term":[70],"Memory":[71],"(LSTM)":[72],"network":[73],"designed":[75],"for":[76],"feature":[77],"extraction":[78],"temporal":[80],"dynamic":[81],"modeling.":[82],"Then":[83,124],"utilize":[85],"classification":[87,141],"subnetwork":[88,120],"classify":[90],"action,":[92],"predict":[94],"status":[96],"of":[97,107,114],"it":[98],"To":[103],"occurrence":[106],"actions":[108],"estimate":[110],"accurate":[112],"time":[113],"occurrence,":[115],"incorporate":[117],"regression":[119,142],"our":[122],"model.":[123],"split":[126],"classes":[129],"three":[131],"stages":[132],"train":[134],"model":[136,151],"by":[137],"optimizing":[138],"joint":[140],"objective":[143],"function.":[144],"Experimental":[145],"results":[146,154],"show":[147],"that":[148],"proposed":[150],"achieves":[152],"satisfactory":[153],"forecast.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}