{"id":"https://openalex.org/W3172257275","doi":"https://doi.org/10.1145/3447587.3447589","title":"Pedestrian Behavior Anomaly Detection Based on Dynamic Mode Decomposition and One-Class SVM","display_name":"Pedestrian Behavior Anomaly Detection Based on Dynamic Mode Decomposition and One-Class SVM","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3172257275","doi":"https://doi.org/10.1145/3447587.3447589","mag":"3172257275"},"language":"en","primary_location":{"id":"doi:10.1145/3447587.3447589","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447587.3447589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 4th International Conference on Image and Graphics Processing","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/A5114444534","display_name":"Zhang Wei-xi","orcid":"https://orcid.org/0009-0001-5388-9478"},"institutions":[{"id":"https://openalex.org/I139024713","display_name":"Guangdong University of Technology","ror":"https://ror.org/04azbjn80","country_code":"CN","type":"education","lineage":["https://openalex.org/I139024713"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhang Weixi","raw_affiliation_strings":["School of Computer Guangdong University of Technology, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Guangdong University of Technology, China","institution_ids":["https://openalex.org/I139024713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101911440","display_name":"Shuai Dong","orcid":"https://orcid.org/0000-0002-9321-5064"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Shuai","raw_affiliation_strings":["Zhongshan Institute University of Electronic Science and Technology of China, China"],"affiliations":[{"raw_affiliation_string":"Zhongshan Institute University of Electronic Science and Technology of China, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035833596","display_name":"Kun Zou","orcid":"https://orcid.org/0000-0002-6481-5458"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zou Kun","raw_affiliation_strings":["University of Electronic Science and Technology of China, China"],"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100653588","display_name":"Wensheng Li","orcid":"https://orcid.org/0000-0002-6639-3156"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Wensheng","raw_affiliation_strings":["Zhongshan Institute University of Electronic Science and Technology of China, China"],"affiliations":[{"raw_affiliation_string":"Zhongshan Institute University of Electronic Science and Technology of China, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5114444534"],"corresponding_institution_ids":["https://openalex.org/I139024713"],"apc_list":null,"apc_paid":null,"fwci":0.136,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.52890964,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"7","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"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":0.9998999834060669,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9959999918937683,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9735999703407288,"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/anomaly-detection","display_name":"Anomaly detection","score":0.8365510702133179},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7694985866546631},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.7428959608078003},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6914060115814209},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5739613175392151},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5215863585472107},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5129201412200928},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.5035926699638367},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.48104336857795715},{"id":"https://openalex.org/keywords/dynamic-mode-decomposition","display_name":"Dynamic mode decomposition","score":0.4539499282836914},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.43473875522613525},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4230210781097412},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4130301773548126},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.4057294726371765},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3615318536758423},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13041046261787415}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.8365510702133179},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7694985866546631},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7428959608078003},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6914060115814209},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5739613175392151},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5215863585472107},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5129201412200928},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.5035926699638367},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.48104336857795715},{"id":"https://openalex.org/C2777032711","wikidata":"https://www.wikidata.org/wiki/Q5318993","display_name":"Dynamic mode decomposition","level":2,"score":0.4539499282836914},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.43473875522613525},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4230210781097412},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4130301773548126},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.4057294726371765},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3615318536758423},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13041046261787415},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447587.3447589","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447587.3447589","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 4th International Conference on Image and Graphics Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W1686810756","https://openalex.org/W1850742715","https://openalex.org/W1923404803","https://openalex.org/W1947481528","https://openalex.org/W2014356541","https://openalex.org/W2020163092","https://openalex.org/W2024868105","https://openalex.org/W2064675550","https://openalex.org/W2067035505","https://openalex.org/W2094686470","https://openalex.org/W2100294832","https://openalex.org/W2119821739","https://openalex.org/W2162915993","https://openalex.org/W2164954534","https://openalex.org/W2234725626","https://openalex.org/W2341174028","https://openalex.org/W2342776425","https://openalex.org/W2531409750","https://openalex.org/W2963282966"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W2597763219","https://openalex.org/W2014356541","https://openalex.org/W2168222921","https://openalex.org/W2992578100","https://openalex.org/W2101960027","https://openalex.org/W2972620127","https://openalex.org/W2981141433","https://openalex.org/W2184115898"],"abstract_inverted_index":{"Anomaly":[0],"detection":[1,127],"of":[2,25,35,51,57,78,84,91,118,164,177,197],"human":[3,20,86],"behavior,":[4],"which":[5,134],"has":[6],"been":[7,46,103],"widely":[8],"applied":[9],"in":[10,13,29,88,105,150],"video":[11,93],"supervision":[12],"recent":[14,106],"years,":[15],"aims":[16],"at":[17],"recognizing":[18],"the":[19,26,33,49,55,76,85,97,109,126,151,154,162,175,186],"behaviors":[21],"that":[22,139,161],"are":[23,94,112,129],"out":[24],"normal":[27],"scope":[28],"high-level":[30],"semantic.":[31],"With":[32],"development":[34],"deep":[36],"learning":[37],"(DL),":[38],"many":[39],"DL-based":[40,58],"methods":[41],"for":[42],"behavior":[43,192],"recognition":[44],"have":[45,102],"developed.":[47],"However,":[48],"lack":[50],"negative":[52,79,143],"samples":[53],"hinders":[54],"application":[56],"methods.":[59],"Thus,":[60],"this":[61],"work":[62],"proposed":[63,187],"a":[64,92,115,136,194],"new":[65],"framework":[66,188],"based":[67],"on":[68],"dynamic":[69],"mode":[70,116],"decomposition":[71],"(DMD)which":[72],"is":[73,135,153],"free":[74],"from":[75],"requirements":[77],"samples.":[80,144,198],"First,":[81],"key":[82,110],"points":[83,111],"body":[87],"each":[89],"frame":[90],"exacted":[95],"by":[96],"-deep":[98],"neural":[99],"networks":[100],"(DNN)which":[101],"solved":[104],"studies.":[107],"Second,":[108],"projected":[113],"into":[114],"space":[117],"much":[119],"lower":[120],"dimensions":[121],"with":[122,131,167,180,193],"DMD.":[123],"At":[124],"last,":[125],"results":[128],"obtained":[130],"One-class":[132],"SVM,":[133],"classification":[137,170],"method":[138],"does":[140],"not":[141],"require":[142],"The":[145],"data":[146,157],"set":[147],"we":[148],"use":[149],"experiment":[152],"CMU":[155],"motion":[156],"set.":[158],"Experiment":[159],"show":[160],"accuracy":[163,176],"DMD":[165,178],"combined":[166,179],"support":[168],"vector":[169],"(SVC)":[171],"achieve":[172,183],"85%":[173],"while":[174],"one-class":[181],"SVM":[182],"81%,":[184],"and":[185],"can":[189],"distinguish":[190],"anomaly":[191],"small":[195],"number":[196]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}