{"id":"https://openalex.org/W3162458784","doi":"https://doi.org/10.1145/3448734.3450926","title":"A Traffic Anomaly Detection and Identification Approach Based on Multi-instance Learning","display_name":"A Traffic Anomaly Detection and Identification Approach Based on Multi-instance Learning","publication_year":2021,"publication_date":"2021-01-28","ids":{"openalex":"https://openalex.org/W3162458784","doi":"https://doi.org/10.1145/3448734.3450926","mag":"3162458784"},"language":"en","primary_location":{"id":"doi:10.1145/3448734.3450926","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3448734.3450926","pdf_url":null,"source":{"id":"https://openalex.org/S4306531993","display_name":"The 2nd International Conference on Computing and Data Science","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2nd International Conference on Computing and Data Science","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/A5003891192","display_name":"Feng Dong","orcid":"https://orcid.org/0000-0002-1485-2402"},"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":true,"raw_author_name":"Dong Feng","raw_affiliation_strings":["Beijing Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034889679","display_name":"Mangui Liang","orcid":"https://orcid.org/0000-0001-8434-8335"},"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":"Mangui Liang","raw_affiliation_strings":["Beijing Jiaotong University"],"affiliations":[{"raw_affiliation_string":"Beijing Jiaotong University","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101668002","display_name":"Guangchao Wang","orcid":"https://orcid.org/0000-0001-8148-493X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guangchao Wang","raw_affiliation_strings":["Qingdao New Generation Artificial Intelligence Technology Research Institute"],"affiliations":[{"raw_affiliation_string":"Qingdao New Generation Artificial Intelligence Technology Research Institute","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5003891192"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03467938,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9973999857902527,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9965000152587891,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/anomaly-detection","display_name":"Anomaly detection","score":0.8362373113632202},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7278347015380859},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.6195510625839233},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.4962967038154602},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45478948950767517},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.44371554255485535},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4270251989364624},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3389563262462616}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8362373113632202},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7278347015380859},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.6195510625839233},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.4962967038154602},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45478948950767517},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.44371554255485535},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4270251989364624},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3389563262462616},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"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/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3448734.3450926","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3448734.3450926","pdf_url":null,"source":{"id":"https://openalex.org/S4306531993","display_name":"The 2nd International Conference on Computing and Data Science","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2nd International Conference on Computing and Data Science","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.49000000953674316,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1967456674","https://openalex.org/W2118572719","https://openalex.org/W2125105611","https://openalex.org/W2141473805","https://openalex.org/W2163612318","https://openalex.org/W2164489414","https://openalex.org/W2225887246","https://openalex.org/W2237765446","https://openalex.org/W2331812431","https://openalex.org/W2520489243","https://openalex.org/W4321353333"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W2972971679"],"abstract_inverted_index":{"Traffic":[0],"anomaly":[1,19,100,117,141,159],"detection":[2,20,101,118,142,149,160],"plays":[3],"an":[4],"important":[5],"role":[6],"in":[7,23,43,119,146,157,161],"effective":[8],"prevention":[9],"and":[10,71,81,111,151,154],"timely":[11],"handling":[12],"of":[13,37,49,74,98,107,115,148],"traffic":[14,18,52,99,116,127,140,158,163],"accidents.":[15],"However,":[16],"currently":[17],"is":[21,41,91,144,155],"still":[22],"its":[24],"infancy,":[25],"mainly":[26],"depending":[27],"on":[28,62,96,125],"manual":[29],"intervention,":[30],"which":[31,103],"not":[32],"only":[33],"consumes":[34],"a":[35,57],"lot":[36],"manpower,":[38],"but":[39],"also":[40],"unfavorable":[42],"timeliness.":[44],"According":[45],"to":[46,67,137],"the":[47,86,94,105,113,138],"characteristics":[48],"urban":[50],"road":[51],"scenes,":[53],"this":[54,132],"paper":[55],"proposes":[56],"MFnet":[58],"network":[59],"structure":[60],"based":[61,124],"multiple":[63],"fiber":[64],"modules,":[65],"aiming":[66],"realize":[68],"fast":[69],"extraction":[70],"real-time":[72],"calculation":[73],"video":[75,128],"streaming":[76],"features":[77],"via":[78],"group":[79],"convolution":[80],"sparse":[82],"connectivity.":[83],"In":[84],"addition,":[85],"weakly-supervised":[87],"multi-instance":[88],"learning":[89],"method":[90,133],"introduced":[92],"for":[93],"training":[95],"application":[97],"model,":[102],"reduces":[104],"difficulty":[106],"labeling":[108],"sample":[109],"videos":[110],"improves":[112],"capacity":[114],"complex":[120],"scenarios.":[121,164],"Experimental":[122],"results":[123],"real":[126],"data":[129],"show":[130],"that":[131],"proposed":[134],"herein,":[135],"compared":[136],"existing":[139],"methods,":[143],"good":[145],"terms":[147],"accuracy":[150],"recall":[152],"rate,":[153],"efficient":[156],"actual":[162]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
