{"id":"https://openalex.org/W2316379514","doi":"https://doi.org/10.1109/tmm.2016.2598091","title":"Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories","display_name":"Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories","publication_year":2016,"publication_date":"2016-08-03","ids":{"openalex":"https://openalex.org/W2316379514","doi":"https://doi.org/10.1109/tmm.2016.2598091","mag":"2316379514"},"language":"en","primary_location":{"id":"doi:10.1109/tmm.2016.2598091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2016.2598091","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1603.09454","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wenxi Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I80947539","display_name":"Fuzhou University","ror":"https://ror.org/011xvna82","country_code":"CN","type":"education","lineage":["https://openalex.org/I80947539"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenxi Liu","raw_affiliation_strings":["Department of Computer Science, Fuzhou University, Fuzhou, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Fuzhou University, Fuzhou, China","institution_ids":["https://openalex.org/I80947539"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Rynson W. H. Lau","orcid":null},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Rynson W. H. Lau","raw_affiliation_strings":["Department of Computer Science, City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaogang Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I177725633","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48","country_code":"CN","type":"education","lineage":["https://openalex.org/I177725633"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaogang Wang","raw_affiliation_strings":["Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"last","author":{"id":null,"display_name":"Dinesh Manocha","orcid":null},"institutions":[{"id":"https://openalex.org/I114027177","display_name":"University of North Carolina at Chapel Hill","ror":"https://ror.org/0130frc33","country_code":"US","type":"education","lineage":["https://openalex.org/I114027177"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dinesh Manocha","raw_affiliation_strings":["Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA","institution_ids":["https://openalex.org/I114027177"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I80947539"],"apc_list":null,"apc_paid":null,"fwci":4.5438,"has_fulltext":false,"cited_by_count":21,"citation_normalized_percentile":{"value":0.9364207,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"18","issue":"12","first_page":"2398","last_page":"2406"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.5795000195503235,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.5795000195503235,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.2671000063419342,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.022600000724196434,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5831999778747559},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5519000291824341},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5311999917030334},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5238000154495239},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5231000185012817},{"id":"https://openalex.org/keywords/crowd-simulation","display_name":"Crowd simulation","score":0.5153999924659729},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5084999799728394},{"id":"https://openalex.org/keywords/crowd-psychology","display_name":"Crowd psychology","score":0.5056999921798706}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8097000122070312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6970999836921692},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5831999778747559},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5519000291824341},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5311999917030334},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C45617602","wikidata":"https://www.wikidata.org/wiki/Q465266","display_name":"Crowd simulation","level":3,"score":0.5153999924659729},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5084999799728394},{"id":"https://openalex.org/C44042526","wikidata":"https://www.wikidata.org/wiki/Q1355183","display_name":"Crowd psychology","level":2,"score":0.5056999921798706},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4625000059604645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.44130000472068787},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4316999912261963},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4262000024318695},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4058000147342682},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40209999680519104},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.39890000224113464},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.37599998712539673},{"id":"https://openalex.org/C2780226923","wikidata":"https://www.wikidata.org/wiki/Q929848","display_name":"Movement (music)","level":2,"score":0.3343999981880188},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C62230096","wikidata":"https://www.wikidata.org/wiki/Q275969","display_name":"Crowdsourcing","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2757999897003174}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tmm.2016.2598091","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmm.2016.2598091","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"},{"id":"pmh:oai:arXiv.org:1603.09454","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1603.09454","pdf_url":"https://arxiv.org/pdf/1603.09454","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1603.09454","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1603.09454","pdf_url":"https://arxiv.org/pdf/1603.09454","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8168621812","display_name":null,"funder_award_id":"2016J05155","funder_id":"https://openalex.org/F4320321878","funder_display_name":"Natural Science Foundation of Fujian Province"}],"funders":[{"id":"https://openalex.org/F4320321878","display_name":"Natural Science Foundation of Fujian Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W100367037","https://openalex.org/W192919555","https://openalex.org/W1482815597","https://openalex.org/W1488202234","https://openalex.org/W1566601044","https://openalex.org/W1970693447","https://openalex.org/W1973873770","https://openalex.org/W1988192097","https://openalex.org/W1989684337","https://openalex.org/W1996097451","https://openalex.org/W2006719702","https://openalex.org/W2009104157","https://openalex.org/W2011261343","https://openalex.org/W2012795032","https://openalex.org/W2014767144","https://openalex.org/W2015410655","https://openalex.org/W2018028329","https://openalex.org/W2019530605","https://openalex.org/W2036721747","https://openalex.org/W2042015300","https://openalex.org/W2043193462","https://openalex.org/W2049302255","https://openalex.org/W2052684427","https://openalex.org/W2067467244","https://openalex.org/W2069829121","https://openalex.org/W2098941887","https://openalex.org/W2106579908","https://openalex.org/W2108404684","https://openalex.org/W2109389234","https://openalex.org/W2117303620","https://openalex.org/W2122361470","https://openalex.org/W2129746290","https://openalex.org/W2146183743","https://openalex.org/W2148633389","https://openalex.org/W2150312211","https://openalex.org/W2160372426","https://openalex.org/W2162616721","https://openalex.org/W2164489414","https://openalex.org/W2167052694","https://openalex.org/W2168866870","https://openalex.org/W6633339542","https://openalex.org/W6659644400","https://openalex.org/W6678706630","https://openalex.org/W6691152312"],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,42,107],"present":[4],"a":[5,28,66,80,110,133],"novel":[6],"method":[7],"to":[8,26,33,36,61,64],"recognize":[9],"the":[10,38,50,54,62,88,92],"types":[11],"of":[12,30],"crowd":[13,16,39,55,67,75,101,118,126,139],"movement":[14,76],"from":[15,103],"trajectories":[17,56,119],"using":[18],"agent-based":[19],"motion":[20,68],"models":[21],"(AMMs).":[22],"Our":[23,84],"idea":[24],"is":[25],"apply":[27],"number":[29],"AMMs,":[31],"referred":[32],"as":[34,79,132],"exemplar-AMMs,":[35],"describe":[37],"movement.":[40],"Specifically,":[41],"propose":[43],"an":[44],"optimization":[45],"framework":[46],"that":[47,87],"filters":[48],"out":[49],"unknown":[51],"noise":[52],"in":[53,95,120],"and":[57,99],"measures":[58],"their":[59,104],"similarity":[60],"exemplar-AMMs":[63],"produce":[65],"feature.":[69],"We":[70],"then":[71],"address":[72],"our":[73],"real-world":[74,100],"recognition":[77],"problem":[78],"multilabel":[81],"classification":[82],"problem.":[83],"experiments":[85],"show":[86],"proposed":[89],"feature":[90],"outperforms":[91],"state-of-the-art":[93],"methods":[94],"recognizing":[96],"both":[97],"simulated":[98],"movements":[102],"trajectories.":[105],"Finally,":[106],"have":[108],"created":[109],"synthetic":[111],"dataset,":[112],"SynCrowd,":[113],"which":[114],"contains":[115],"two-dimensional":[116],"(2D)":[117],"various":[121,125],"scenarios,":[122],"generated":[123],"by":[124],"simulators.":[127],"This":[128],"dataset":[129],"can":[130],"serve":[131],"training":[134],"set":[135],"or":[136],"benchmark":[137],"for":[138],"analysis":[140],"work.":[141]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2}],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2016-06-24T00:00:00"}
