{"id":"https://openalex.org/W2774062856","doi":"https://doi.org/10.1145/3155133.3155147","title":"Abnormal Activity Detection based on Dense Spatial-Temporal Features and Improved One-Class Learning","display_name":"Abnormal Activity Detection based on Dense Spatial-Temporal Features and Improved One-Class Learning","publication_year":2017,"publication_date":"2017-12-07","ids":{"openalex":"https://openalex.org/W2774062856","doi":"https://doi.org/10.1145/3155133.3155147","mag":"2774062856"},"language":"en","primary_location":{"id":"doi:10.1145/3155133.3155147","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3155133.3155147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth International Symposium on Information and Communication Technology","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/A5080881429","display_name":"Tam N. Nguyen","orcid":"https://orcid.org/0000-0002-8577-8342"},"institutions":[{"id":"https://openalex.org/I123565023","display_name":"Vietnam National University Ho Chi Minh City","ror":"https://ror.org/00waaqh38","country_code":"VN","type":"education","lineage":["https://openalex.org/I123565023"]},{"id":"https://openalex.org/I23582244","display_name":"Ho Chi Minh City University of Science","ror":"https://ror.org/05jfbgm49","country_code":"VN","type":"education","lineage":["https://openalex.org/I123565023","https://openalex.org/I23582244"]}],"countries":["VN"],"is_corresponding":true,"raw_author_name":"Tam N. Nguyen","raw_affiliation_strings":["University of Science, VNU-HCMC, Vietnam"],"affiliations":[{"raw_affiliation_string":"University of Science, VNU-HCMC, Vietnam","institution_ids":["https://openalex.org/I23582244","https://openalex.org/I123565023"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024762397","display_name":"Ngoc Quoc Ly","orcid":"https://orcid.org/0000-0003-4476-2456"},"institutions":[{"id":"https://openalex.org/I23582244","display_name":"Ho Chi Minh City University of Science","ror":"https://ror.org/05jfbgm49","country_code":"VN","type":"education","lineage":["https://openalex.org/I123565023","https://openalex.org/I23582244"]},{"id":"https://openalex.org/I123565023","display_name":"Vietnam National University Ho Chi Minh City","ror":"https://ror.org/00waaqh38","country_code":"VN","type":"education","lineage":["https://openalex.org/I123565023"]}],"countries":["VN"],"is_corresponding":false,"raw_author_name":"Ngoc Q. Ly","raw_affiliation_strings":["University of Science, VNU-HCMC, Vietnam"],"affiliations":[{"raw_affiliation_string":"University of Science, VNU-HCMC, Vietnam","institution_ids":["https://openalex.org/I23582244","https://openalex.org/I123565023"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080881429"],"corresponding_institution_ids":["https://openalex.org/I123565023","https://openalex.org/I23582244"],"apc_list":null,"apc_paid":null,"fwci":0.7801,"has_fulltext":false,"cited_by_count":13,"citation_normalized_percentile":{"value":0.80190387,"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":"370","last_page":"377"},"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":0.9994999766349792,"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":0.9990000128746033,"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":0.7092931270599365},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7060760259628296},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.6513960361480713},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6450998187065125},{"id":"https://openalex.org/keywords/hypersphere","display_name":"Hypersphere","score":0.6415076851844788},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5882344245910645},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5850701332092285},{"id":"https://openalex.org/keywords/activity-detection","display_name":"Activity detection","score":0.5463888049125671},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.5186007022857666},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4804108440876007},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.43058478832244873},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.42143067717552185}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7092931270599365},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7060760259628296},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6513960361480713},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6450998187065125},{"id":"https://openalex.org/C2776562905","wikidata":"https://www.wikidata.org/wiki/Q306610","display_name":"Hypersphere","level":2,"score":0.6415076851844788},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5882344245910645},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5850701332092285},{"id":"https://openalex.org/C2988656282","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity detection","level":2,"score":0.5463888049125671},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.5186007022857666},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4804108440876007},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43058478832244873},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.42143067717552185},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3155133.3155147","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3155133.3155147","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Eighth International Symposium on Information and Communication Technology","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W842715210","https://openalex.org/W1495344989","https://openalex.org/W1573308499","https://openalex.org/W1970088130","https://openalex.org/W1983643426","https://openalex.org/W1988192097","https://openalex.org/W2032233621","https://openalex.org/W2033810668","https://openalex.org/W2054554150","https://openalex.org/W2061360510","https://openalex.org/W2066941820","https://openalex.org/W2068611653","https://openalex.org/W2105497548","https://openalex.org/W2108598075","https://openalex.org/W2122361470","https://openalex.org/W2153635508","https://openalex.org/W2164489414","https://openalex.org/W2460849547","https://openalex.org/W2531279268","https://openalex.org/W2560335610","https://openalex.org/W2613487644","https://openalex.org/W2929860100","https://openalex.org/W6675613107"],"related_works":["https://openalex.org/W2896666051","https://openalex.org/W2944566775","https://openalex.org/W2279827266","https://openalex.org/W2880976449","https://openalex.org/W2922650392","https://openalex.org/W2017526120","https://openalex.org/W1983447325","https://openalex.org/W2003968596","https://openalex.org/W2067125787","https://openalex.org/W2999825330"],"abstract_inverted_index":{"Abnormal":[0],"activity":[1,12,17,26,52],"detection":[2],"is":[3,60,66],"an":[4],"important":[5],"issue":[6],"in":[7],"video":[8],"surveillance.":[9],"The":[10,142],"abnormal":[11,51,71],"could":[13,42,148],"be":[14],"a":[15],"predictable":[16],"or":[18],"unpredictable":[19,25,30,70],"activity.":[20,55],"This":[21],"paper":[22],"focuses":[23],"on":[24,114,122,126,156],"detection.":[27],"Due":[28],"to":[29,49,68,87,102],"anomalies,":[31],"we":[32,41,76,98],"do":[33],"not":[34,43],"have":[35],"training":[36],"data":[37,105],"of":[38,84,117,152],"them,":[39],"so":[40],"use":[44,77,99],"the":[45,61,107,153,157],"discriminative":[46],"learning":[47,58],"model":[48,63,69,88],"detect":[50],"and":[53,64,139],"normal":[54,89,104],"One":[56],"class":[57],"method":[59],"generative":[62],"it":[65],"suitable":[67],"activities.":[72],"In":[73],"this":[74],"paper,":[75],"fast":[78],"dense":[79],"spatial-temporal":[80],"features":[81],"within":[82],"regions":[83],"interest":[85],"points":[86],"activities":[90],"by":[91],"Support":[92],"Vector":[93],"Data":[94],"Description":[95],"(SVDD).":[96],"Besides.":[97],"K-means++":[100],"algorithm":[101],"cluster":[103],"then":[106],"multi":[108],"hyperspheres":[109],"SVDD":[110,121],"are":[111],"constructed":[112],"separately":[113],"clusters":[115],"instead":[116],"only":[118],"one":[119],"hypersphere":[120],"multi-distribution":[123],"data.":[124],"Experiments":[125],"benchmark":[127],"datasets":[128],"contain":[129],"various":[130],"situations":[131],"with":[132],"human":[133],"crowds,":[134],"overlapping":[135],"between":[136],"individual":[137],"subjects":[138],"low":[140],"resolution.":[141],"experiments":[143],"show":[144],"that":[145],"our":[146],"approach":[147],"outperform":[149],"some":[150],"state":[151],"art":[154],"methods":[155],"Ped2":[158],"dataset.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
