{"id":"https://openalex.org/W2576251866","doi":"https://doi.org/10.1109/mmsp.2016.7813390","title":"Global anomaly detection in crowded scenes based on optical flow saliency","display_name":"Global anomaly detection in crowded scenes based on optical flow saliency","publication_year":2016,"publication_date":"2016-09-01","ids":{"openalex":"https://openalex.org/W2576251866","doi":"https://doi.org/10.1109/mmsp.2016.7813390","mag":"2576251866"},"language":"en","primary_location":{"id":"doi:10.1109/mmsp.2016.7813390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2016.7813390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)","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/A5100413570","display_name":"Ang Li","orcid":"https://orcid.org/0000-0001-5690-8307"},"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":"Ang Li","raw_affiliation_strings":["Institute of Information Science Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111825657","display_name":"Zhenjiang Miao","orcid":null},"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, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112135488","display_name":"Yigang Cen","orcid":null},"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":"Yigang Cen","raw_affiliation_strings":["Institute of Information Science Beijing Jiaotong University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Information Science Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100413570"],"corresponding_institution_ids":["https://openalex.org/I21193070"],"apc_list":null,"apc_paid":null,"fwci":0.8569,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.85008476,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"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":0.9936000108718872,"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/T11819","display_name":"Data-Driven Disease Surveillance","score":0.9638000130653381,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.708321750164032},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.7047133445739746},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5883256196975708},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5574938058853149},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5208519101142883},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4860301613807678},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4453818202018738},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.228519469499588},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1530102789402008}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.708321750164032},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.7047133445739746},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5883256196975708},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5574938058853149},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5208519101142883},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4860301613807678},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4453818202018738},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.228519469499588},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1530102789402008},{"id":"https://openalex.org/C57879066","wikidata":"https://www.wikidata.org/wiki/Q41217","display_name":"Mechanics","level":1,"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mmsp.2016.7813390","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mmsp.2016.7813390","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5600000023841858,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W31178829","https://openalex.org/W1829684718","https://openalex.org/W2005876975","https://openalex.org/W2012931101","https://openalex.org/W2054554150","https://openalex.org/W2065994824","https://openalex.org/W2067700376","https://openalex.org/W2074076772","https://openalex.org/W2107323262","https://openalex.org/W2124386111","https://openalex.org/W2127271355","https://openalex.org/W2154693947","https://openalex.org/W2160547390","https://openalex.org/W2164489414"],"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/W3030345572"],"abstract_inverted_index":{"In":[0,102],"this":[1],"paper,":[2],"an":[3,71],"algorithm":[4,66,162],"of":[5,24,28,48,55,59,80,97,121,136],"global":[6],"anomaly":[7,135],"detection":[8],"in":[9,15],"crowded":[10],"scenes":[11],"using":[12],"the":[13,22,26,34,45,53,56,62,82,89,94,98,115,122,127,134,137,153,156],"saliency":[14,46],"optical":[16,30,49],"flow":[17,31,50],"field":[18],"is":[19,41,67,86,109,141,163],"proposed.":[20],"Before":[21],"process":[23,79],"extracting":[25],"histogram":[27],"maximal":[29],"projection":[32],"(HMOFP),":[33],"scale":[35],"invariant":[36],"feature":[37,58,96],"transforms":[38],"(SIFT)":[39],"method":[40],"utilized":[42],"to":[43,69,104,132,155],"get":[44],"map":[47],"field.":[51],"On":[52],"basis":[54],"HMOFP":[57,95],"normal":[60,110],"frames,":[61],"online":[63],"dictionary":[64,73,90],"learning":[65],"used":[68],"train":[70],"optimal":[72],"with":[74],"proper":[75],"redundancy":[76],"after":[77],"a":[78,107],"selecting":[81],"training":[83,100],"samples,":[84],"which":[85,140],"better":[87],"than":[88],"simply":[91],"composed":[92],"by":[93],"whole":[99],"frames.":[101],"order":[103],"detect":[105],"whether":[106],"frame":[108],"or":[111],"not,":[112],"we":[113],"use":[114],"\u2113":[116],"<sub":[117],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[118],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sub>":[119],"-norm":[120],"sparse":[123,128],"reconstruction":[124,129],"coefficients":[125],"(i.e.,":[126],"cost,":[130],"SRC)":[131],"show":[133,159],"testing":[138],"frame,":[139],"simple":[142],"but":[143],"very":[144],"effective.":[145],"The":[146],"experiment":[147],"results":[148],"on":[149],"UMN":[150],"dataset":[151],"and":[152],"comparison":[154],"state-of-the-art":[157],"methods":[158],"that":[160],"our":[161],"promising.":[164]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}