{"id":"https://openalex.org/W2696706365","doi":"https://doi.org/10.1109/icassp.2017.7952428","title":"Learning a hierarchical spatio-temporal model for human activity recognition","display_name":"Learning a hierarchical spatio-temporal model for human activity recognition","publication_year":2017,"publication_date":"2017-03-01","ids":{"openalex":"https://openalex.org/W2696706365","doi":"https://doi.org/10.1109/icassp.2017.7952428","mag":"2696706365"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2017.7952428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952428","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":"conference-paper","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/A5071889746","display_name":"Wanru Xu","orcid":"https://orcid.org/0000-0003-2206-5051"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanru Xu","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100298934","display_name":"Zhenjiang Miao","orcid":"https://orcid.org/0000-0001-8032-5769"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenjiang Miao","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100363146","display_name":"Xiao\u2013Ping Zhang","orcid":"https://orcid.org/0000-0001-5241-0069"},"institutions":[{"id":"https://openalex.org/I530967","display_name":"Toronto Metropolitan University","ror":"https://ror.org/05g13zd79","country_code":"CA","type":"education","lineage":["https://openalex.org/I530967"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xiao-Ping Zhang","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Ryerson University, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Ryerson University, Canada","institution_ids":["https://openalex.org/I530967"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100752649","display_name":"Yi Tian","orcid":"https://orcid.org/0000-0001-6054-7970"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Tian","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4776,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.75991371,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1607","last_page":"1611"},"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":0.9979000091552734,"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/T10444","display_name":"Context-Aware Activity Recognition Systems","score":0.9951000213623047,"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/discriminative-model","display_name":"Discriminative model","score":0.9049811363220215},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7552435398101807},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6809499263763428},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6619625687599182},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.6425389051437378},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6147581338882446},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5914619565010071},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5607790946960449},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5585348606109619},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5549581050872803},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5397989749908447},{"id":"https://openalex.org/keywords/hierarchical-database-model","display_name":"Hierarchical database model","score":0.4972894489765167},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.439005970954895},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.426662415266037},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2658015787601471},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09421709179878235}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.9049811363220215},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7552435398101807},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6809499263763428},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6619625687599182},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.6425389051437378},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6147581338882446},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5914619565010071},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5607790946960449},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5585348606109619},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5549581050872803},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5397989749908447},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.4972894489765167},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.439005970954895},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.426662415266037},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2658015787601471},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09421709179878235},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2017.7952428","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2017.7952428","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":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1892072966","https://openalex.org/W1923332106","https://openalex.org/W1941902227","https://openalex.org/W1944615693","https://openalex.org/W1967934172","https://openalex.org/W1976323859","https://openalex.org/W1979393784","https://openalex.org/W1980956450","https://openalex.org/W1989560997","https://openalex.org/W1999192586","https://openalex.org/W2057443918","https://openalex.org/W2066757459","https://openalex.org/W2101194540","https://openalex.org/W2114361838","https://openalex.org/W2136917337","https://openalex.org/W2140978750","https://openalex.org/W2149084152","https://openalex.org/W2152239535","https://openalex.org/W2156372729","https://openalex.org/W2169393274","https://openalex.org/W2533739470","https://openalex.org/W2962727177","https://openalex.org/W4249279051","https://openalex.org/W6677137762","https://openalex.org/W6680698335","https://openalex.org/W6693913639"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4312814274","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W3030749275"],"abstract_inverted_index":{"Recent":[0],"works":[1],"have":[2],"shown":[3],"that":[4,126],"hierarchical":[5,43,55,107],"models":[6],"lead":[7],"to":[8,49,93],"significant":[9],"improvement":[10],"in":[11,78,97],"human":[12,132],"activity":[13],"recognition,":[14],"which":[15],"can":[16,129],"not":[17],"only":[18],"enhance":[19],"descriptive":[20,68],"capability,":[21],"but":[22],"also":[23],"improve":[24],"discriminative":[25,111],"power.":[26,112],"However,":[27],"most":[28],"existing":[29],"methods":[30],"exploit":[31],"just":[32],"one":[33,61],"of":[34,119],"the":[35,60,63,86,98,103,106,127],"two":[36],"advantages.":[37],"In":[38],"this":[39],"paper,":[40],"a":[41],"new":[42],"spatio-temporal":[44],"model":[45,57,65,108],"(HSTM)":[46],"is":[47],"proposed":[48],"integrate":[50],"feature":[51],"learning":[52,82],"into":[53],"two-layer":[54,64],"classification":[56],"simultaneously.":[58],"On":[59,102],"hand,":[62,105],"has":[66,109],"sufficient":[67],"capability.":[69],"The":[70],"bottom":[71],"layer":[72,88],"aims":[73],"at":[74],"capturing":[75],"spatial":[76,114],"relations":[77,96],"each":[79],"frame":[80],"and":[81,85,116,141,146],"high-level":[83],"representations,":[84],"top":[87],"utilizes":[89],"these":[90],"learned":[91],"features":[92],"characterize":[94],"temporal":[95,117],"whole":[99],"video":[100],"sequence.":[101],"other":[104],"strong":[110],"Both":[113],"similarity":[115,118],"activities":[120,133,149],"are":[121],"measured.":[122],"Experimental":[123],"results":[124],"show":[125],"HSTM":[128],"successfully":[130],"recognize":[131],"with":[134],"higher":[135],"accuracies":[136],"on":[137],"one-person":[138],"actions":[139],"(KTH":[140],"UCF),":[142],"human-human":[143],"interactions":[144],"(CASIA),":[145],"human-object":[147],"interactional":[148],"(Gupta).":[150]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
