{"id":"https://openalex.org/W1922838137","doi":"https://doi.org/10.1109/cvpr.2015.7298971","title":"Understanding pedestrian behaviors from stationary crowd groups","display_name":"Understanding pedestrian behaviors from stationary crowd groups","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W1922838137","doi":"https://doi.org/10.1109/cvpr.2015.7298971","mag":"1922838137"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2015.7298971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","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/A5101696728","display_name":"Shuai Yi","orcid":"https://orcid.org/0000-0002-1253-6633"},"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":true,"raw_author_name":"Shuai Yi","raw_affiliation_strings":["Department of Electronic Engineering, The Chinese University, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, The Chinese University, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100732450","display_name":"Hongsheng Li","orcid":"https://orcid.org/0000-0002-2664-7975"},"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"]},{"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":"Hongsheng Li","raw_affiliation_strings":["Department of Electronic Engineering, The Chinese University, Hong Kong","School of Electronic Engineering, University of Electronic Science and Technology, China"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, The Chinese University, Hong Kong","institution_ids":["https://openalex.org/I177725633"]},{"raw_affiliation_string":"School of Electronic Engineering, University of Electronic Science and Technology, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100444820","display_name":"Xiaogang Wang","orcid":"https://orcid.org/0000-0002-7929-5889"},"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, The Chinese University, Hong Kong"],"affiliations":[{"raw_affiliation_string":"Department of Electronic Engineering, The Chinese University, Hong Kong","institution_ids":["https://openalex.org/I177725633"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101696728"],"corresponding_institution_ids":["https://openalex.org/I177725633"],"apc_list":null,"apc_paid":null,"fwci":14.5895,"has_fulltext":false,"cited_by_count":239,"citation_normalized_percentile":{"value":0.99249516,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3488","last_page":"3496"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"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"}},"topics":[{"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/T11500","display_name":"Evacuation and Crowd Dynamics","score":0.9990000128746033,"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":0.9976999759674072,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.9161165952682495},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6911383271217346},{"id":"https://openalex.org/keywords/crowd-simulation","display_name":"Crowd simulation","score":0.6114802956581116},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5847086906433105},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5643512606620789},{"id":"https://openalex.org/keywords/crowd-psychology","display_name":"Crowd psychology","score":0.5421702265739441},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5035013556480408},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4365643262863159},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4365389943122864},{"id":"https://openalex.org/keywords/crowds","display_name":"Crowds","score":0.36143696308135986},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3451346158981323},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.32934412360191345},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.20550569891929626},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.16073697805404663},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1330946385860443}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.9161165952682495},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6911383271217346},{"id":"https://openalex.org/C45617602","wikidata":"https://www.wikidata.org/wiki/Q465266","display_name":"Crowd simulation","level":3,"score":0.6114802956581116},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5847086906433105},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5643512606620789},{"id":"https://openalex.org/C44042526","wikidata":"https://www.wikidata.org/wiki/Q1355183","display_name":"Crowd psychology","level":2,"score":0.5421702265739441},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5035013556480408},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4365643262863159},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4365389943122864},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.36143696308135986},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3451346158981323},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.32934412360191345},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.20550569891929626},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.16073697805404663},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1330946385860443},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cvpr.2015.7298971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2015.7298971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.714.4706","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.714.4706","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ee.cuhk.edu.hk/%7Exgwang/papers/yiLWcvpr15.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1482815597","https://openalex.org/W1577818934","https://openalex.org/W1775342812","https://openalex.org/W1888172398","https://openalex.org/W1910776219","https://openalex.org/W1962468782","https://openalex.org/W1968362113","https://openalex.org/W1994987524","https://openalex.org/W1996097451","https://openalex.org/W1999244633","https://openalex.org/W2005256556","https://openalex.org/W2006719702","https://openalex.org/W2006720668","https://openalex.org/W2014767144","https://openalex.org/W2023048829","https://openalex.org/W2036536917","https://openalex.org/W2036721747","https://openalex.org/W2040395913","https://openalex.org/W2042015300","https://openalex.org/W2047499569","https://openalex.org/W2049302255","https://openalex.org/W2055548668","https://openalex.org/W2057067088","https://openalex.org/W2067467244","https://openalex.org/W2089584121","https://openalex.org/W2092272787","https://openalex.org/W2108404684","https://openalex.org/W2109389234","https://openalex.org/W2121839066","https://openalex.org/W2125095437","https://openalex.org/W2128534087","https://openalex.org/W2134080476","https://openalex.org/W2134944993","https://openalex.org/W2142125366","https://openalex.org/W2145508099","https://openalex.org/W2148875485","https://openalex.org/W2149021215","https://openalex.org/W2150704630","https://openalex.org/W2154642173","https://openalex.org/W2160372426","https://openalex.org/W2164489414","https://openalex.org/W2167052694","https://openalex.org/W2532516272","https://openalex.org/W2534527426","https://openalex.org/W2539222059","https://openalex.org/W3125996844","https://openalex.org/W4252035539","https://openalex.org/W6628681060","https://openalex.org/W6639759915","https://openalex.org/W6664757799","https://openalex.org/W6678706630","https://openalex.org/W6680330856","https://openalex.org/W6681310799"],"related_works":["https://openalex.org/W2189920449","https://openalex.org/W3096912411","https://openalex.org/W4376137772","https://openalex.org/W3043995842","https://openalex.org/W2077952221","https://openalex.org/W2218711042","https://openalex.org/W2036586770","https://openalex.org/W2314427365","https://openalex.org/W2094931707","https://openalex.org/W172068003"],"abstract_inverted_index":{"Pedestrian":[0],"behavior":[1,45,143],"modeling":[2,46],"and":[3,11,65,95,137,145],"analysis":[4,144],"is":[5,41,82,112],"important":[6],"for":[7,43],"crowd":[8,19,50,63,124,146],"scene":[9,147],"understanding":[10],"has":[12],"various":[13],"applications":[14],"in":[15,33],"video":[16,126],"surveillance.":[17],"Stationary":[18],"groups":[20,51,64],"are":[21,127],"a":[22,38,53,103,122],"key":[23,54],"factor":[24],"influencing":[25],"pedestrian":[26,44,74,105,142],"walking":[27,88,106,115],"patterns":[28],"but":[29],"was":[30],"largely":[31],"ignored":[32],"literature.":[34],"In":[35],"this":[36],"paper,":[37],"novel":[39],"model":[40,68,81],"proposed":[42,80],"by":[47],"including":[48,87],"stationary":[49,62],"as":[52],"component.":[55],"Through":[56],"inference":[57],"on":[58,141],"the":[59,79,135],"interactions":[60],"between":[61],"pedestrians,":[66],"our":[67,101],"can":[69],"be":[70,132],"used":[71],"to":[72,134],"investigate":[73],"behaviors.":[75],"The":[76,114],"effectiveness":[77],"of":[78,117],"demonstrated":[83],"through":[84],"multiple":[85],"applications,":[86],"path":[89],"prediction,":[90,92],"destination":[91],"personality":[93],"classification,":[94],"abnormal":[96],"event":[97],"detection.":[98],"To":[99],"evaluate":[100],"model,":[102],"large":[104],"route":[107],"dataset":[108],"<sup":[109],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[110],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[111],"built.":[113],"routes":[116],"12,":[118],"684":[119],"pedestrians":[120],"from":[121],"one-hour":[123],"surveillance":[125],"manually":[128],"annotated.":[129],"It":[130],"will":[131],"released":[133],"public":[136],"benefit":[138],"future":[139],"research":[140],"understanding.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":16},{"year":2023,"cited_by_count":20},{"year":2022,"cited_by_count":23},{"year":2021,"cited_by_count":22},{"year":2020,"cited_by_count":26},{"year":2019,"cited_by_count":37},{"year":2018,"cited_by_count":32},{"year":2017,"cited_by_count":26},{"year":2016,"cited_by_count":18},{"year":2015,"cited_by_count":2}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}