{"id":"https://openalex.org/W2808699053","doi":"https://doi.org/10.1109/tmm.2018.2846411","title":"Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos","display_name":"Sparse Coding Guided Spatiotemporal Feature Learning for Abnormal Event Detection in Large Videos","publication_year":2018,"publication_date":"2018-06-11","ids":{"openalex":"https://openalex.org/W2808699053","doi":"https://doi.org/10.1109/tmm.2018.2846411","mag":"2808699053"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2018.2846411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2018.2846411","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-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/A5017381951","display_name":"Wenqing Chu","orcid":"https://orcid.org/0000-0003-0816-7975"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenqing Chu","raw_affiliation_strings":["State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-0816-7975","affiliations":[{"raw_affiliation_string":"State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088930335","display_name":"Hongyang Xue","orcid":"https://orcid.org/0000-0003-3161-3566"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongyang Xue","raw_affiliation_strings":["State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-3161-3566","affiliations":[{"raw_affiliation_string":"State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058133483","display_name":"Chengwei Yao","orcid":"https://orcid.org/0000-0002-6035-1502"},"institutions":[{"id":"https://openalex.org/I168879160","display_name":"Zhejiang University of Science and Technology","ror":"https://ror.org/05mx0wr29","country_code":"CN","type":"education","lineage":["https://openalex.org/I168879160"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chengwei Yao","raw_affiliation_strings":["College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-6035-1502","affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I168879160"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037942269","display_name":"Deng Cai","orcid":"https://orcid.org/0000-0001-9817-4065"},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deng Cai","raw_affiliation_strings":["State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-9817-4065","affiliations":[{"raw_affiliation_string":"State Key Lab of CAD&CG, College of Computer Science and Technology, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.2006,"has_fulltext":false,"cited_by_count":134,"citation_normalized_percentile":{"value":0.98676594,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"21","issue":"1","first_page":"246","last_page":"255"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"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":0.9876000285148621,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.982699990272522,"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/computer-science","display_name":"Computer science","score":0.8490880727767944},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.695597767829895},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.6824121475219727},{"id":"https://openalex.org/keywords/neural-coding","display_name":"Neural coding","score":0.5904632806777954},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5796927213668823},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5219787359237671},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49326056241989136},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.48169857263565063},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.478145956993103},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4468749165534973},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.43502867221832275},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.42152926325798035},{"id":"https://openalex.org/keywords/coding","display_name":"Coding (social sciences)","score":0.41000980138778687}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8490880727767944},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.695597767829895},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.6824121475219727},{"id":"https://openalex.org/C77637269","wikidata":"https://www.wikidata.org/wiki/Q7002051","display_name":"Neural coding","level":2,"score":0.5904632806777954},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5796927213668823},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5219787359237671},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49326056241989136},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48169857263565063},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.478145956993103},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4468749165534973},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.43502867221832275},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.42152926325798035},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.41000980138778687},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tmm.2018.2846411","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2018.2846411","pdf_url":null,"source":{"id":"https://openalex.org/S137030581","display_name":"IEEE Transactions on Multimedia","issn_l":"1520-9210","issn":["1520-9210","1941-0077"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Multimedia","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G6887114618","display_name":null,"funder_award_id":"61751307","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":95,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W343636949","https://openalex.org/W1483393862","https://openalex.org/W1484830317","https://openalex.org/W1522734439","https://openalex.org/W1573040851","https://openalex.org/W1980508392","https://openalex.org/W1983103633","https://openalex.org/W1991251598","https://openalex.org/W1991597092","https://openalex.org/W2012931101","https://openalex.org/W2015461918","https://openalex.org/W2016053056","https://openalex.org/W2021659075","https://openalex.org/W2041259983","https://openalex.org/W2041390734","https://openalex.org/W2047016883","https://openalex.org/W2055600015","https://openalex.org/W2095640719","https://openalex.org/W2097363716","https://openalex.org/W2102605133","https://openalex.org/W2110934250","https://openalex.org/W2111918405","https://openalex.org/W2112680570","https://openalex.org/W2112832339","https://openalex.org/W2122361470","https://openalex.org/W2122429065","https://openalex.org/W2125105611","https://openalex.org/W2129520225","https://openalex.org/W2130349088","https://openalex.org/W2132670931","https://openalex.org/W2133710149","https://openalex.org/W2138092272","https://openalex.org/W2142412278","https://openalex.org/W2145094598","https://openalex.org/W2148301747","https://openalex.org/W2151096523","https://openalex.org/W2151103935","https://openalex.org/W2156303437","https://openalex.org/W2160547390","https://openalex.org/W2163352848","https://openalex.org/W2163605009","https://openalex.org/W2163612318","https://openalex.org/W2164261375","https://openalex.org/W2164489414","https://openalex.org/W2170932168","https://openalex.org/W2254513182","https://openalex.org/W2315889990","https://openalex.org/W2341058432","https://openalex.org/W2507009361","https://openalex.org/W2508191294","https://openalex.org/W2508497007","https://openalex.org/W2518666399","https://openalex.org/W2518754566","https://openalex.org/W2519730330","https://openalex.org/W2519750370","https://openalex.org/W2523332653","https://openalex.org/W2529163075","https://openalex.org/W2530717046","https://openalex.org/W2549699157","https://openalex.org/W2559325834","https://openalex.org/W2559542432","https://openalex.org/W2579718262","https://openalex.org/W2587789887","https://openalex.org/W2606144683","https://openalex.org/W2618530766","https://openalex.org/W2735159761","https://openalex.org/W2739928746","https://openalex.org/W2962791923","https://openalex.org/W2962852342","https://openalex.org/W2963058520","https://openalex.org/W2963420272","https://openalex.org/W2963899855","https://openalex.org/W2964167449","https://openalex.org/W2964189431","https://openalex.org/W2964214371","https://openalex.org/W2997574889","https://openalex.org/W3101203783","https://openalex.org/W3143144647","https://openalex.org/W4280568208","https://openalex.org/W6629027181","https://openalex.org/W6678407962","https://openalex.org/W6681096077","https://openalex.org/W6682864246","https://openalex.org/W6687506355","https://openalex.org/W6687869393","https://openalex.org/W6691489322","https://openalex.org/W6724944384","https://openalex.org/W6724997344","https://openalex.org/W6726076484","https://openalex.org/W6726541112","https://openalex.org/W6727331980","https://openalex.org/W6731992678","https://openalex.org/W6736351416","https://openalex.org/W6990529707"],"related_works":["https://openalex.org/W2890544631","https://openalex.org/W2067062989","https://openalex.org/W2998105788","https://openalex.org/W4205656132","https://openalex.org/W2111634407","https://openalex.org/W3004790527","https://openalex.org/W2203155458","https://openalex.org/W2783282829","https://openalex.org/W4285233543","https://openalex.org/W2138494306"],"abstract_inverted_index":{"Abnormal":[0],"event":[1,78],"detection":[2,38,79],"in":[3,10,20,257],"large":[4],"videos":[5],"is":[6,53,105,238],"an":[7,36],"important":[8],"task":[9],"research":[11],"and":[12,33,200,216,225],"industrial":[13],"applications,":[14],"which":[15,52,170],"has":[16],"attracted":[17],"considerable":[18],"attention":[19],"recent":[21],"years.":[22],"Existing":[23],"methods":[24],"usually":[25],"solve":[26],"this":[27,64,201],"problem":[28,104],"by":[29],"extracting":[30],"local":[31],"features":[32,126,195],"then":[34],"learning":[35,73],"outlier":[37],"model":[39,165],"on":[40,230,250],"training":[41,181],"videos.":[42],"However,":[43],"most":[44],"previous":[45],"approaches":[46],"merely":[47],"employ":[48,118],"hand-crafted":[49,125],"visual":[50],"features,":[51],"a":[54,68,140,176,260],"clear":[55],"disadvantage":[56],"due":[57],"to":[58,97,107,131,148,164,174,192,240],"their":[59],"limited":[60],"representation":[61],"capacity.":[62],"In":[63,156],"paper,":[65],"we":[66,89,117,138,159,221],"present":[67],"novel":[69],"unsupervised":[70,134,218],"deep":[71,92],"feature":[72,99,135,219,227],"algorithm":[74,269],"for":[75,180,196,206],"the":[76,83,87,91,102,109,119,124,129,133,149,153,157,161,166,182,186,194,213,217,231,234,242,267,273],"abnormal":[77],"problem.":[80],"To":[81],"exploit":[82],"spatiotemporal":[84],"information":[85,151],"of":[86,123,152,245,262],"inputs,":[88],"utilize":[90],"three-dimensional":[93],"convolutional":[94],"network":[95,111,188],"(C3D)":[96],"perform":[98],"extraction.":[100],"Then,":[101],"key":[103],"how":[106],"train":[108],"C3D":[110,183,187],"without":[112],"any":[113],"category":[114],"labels.":[115],"Here,":[116],"sparse":[120,197,214,235],"coding":[121,198,215],"results":[122],"generated":[127],"from":[128],"inputs":[130,146],"guide":[132],"learning.":[136],"Specifically,":[137],"define":[139],"multilevel":[141,167],"similarity":[142,168],"relationship":[143],"between":[144,212],"these":[145],"according":[147],"statistical":[150],"shared":[154],"atoms.":[155],"following,":[158],"introduce":[160],"quadruplet":[162],"concept":[163],"structure,":[169],"could":[171,189,203],"be":[172,190,204],"used":[173],"construct":[175],"generalized":[177],"triplet":[178],"loss":[179],"network.":[184],"Furthermore,":[185],"utilized":[191],"generate":[193],"again,":[199],"pipeline":[202],"iterated":[205],"several":[207,251],"times.":[208],"By":[209],"jointly":[210],"optimizing":[211],"learning,":[220],"can":[222],"obtain":[223],"robust":[224],"rich":[226],"representations.":[228],"Based":[229],"learned":[232],"representations,":[233],"reconstruction":[236],"error":[237],"applied":[239],"predicting":[241],"anomaly":[243],"score":[244],"each":[246],"testing":[247],"input.":[248],"Experiments":[249],"publicly":[252],"available":[253],"video":[254],"surveillance":[255],"datasets":[256],"comparison":[258],"with":[259],"number":[261],"existing":[263],"works":[264],"demonstrate":[265],"that":[266],"proposed":[268],"performs":[270],"favorably":[271],"against":[272],"state-of-the-art":[274],"methods.":[275]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":18},{"year":2023,"cited_by_count":19},{"year":2022,"cited_by_count":25},{"year":2021,"cited_by_count":16},{"year":2020,"cited_by_count":27},{"year":2019,"cited_by_count":16},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}
