{"id":"https://openalex.org/W3119615601","doi":"https://doi.org/10.1109/icarcv50220.2020.9305476","title":"Mid-level Features for Categorization of Social Interactions in Public Spaces","display_name":"Mid-level Features for Categorization of Social Interactions in Public Spaces","publication_year":2020,"publication_date":"2020-12-13","ids":{"openalex":"https://openalex.org/W3119615601","doi":"https://doi.org/10.1109/icarcv50220.2020.9305476","mag":"3119615601"},"language":"en","primary_location":{"id":"doi:10.1109/icarcv50220.2020.9305476","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv50220.2020.9305476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)","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/A5038092547","display_name":"M. Sami Zitouni","orcid":"https://orcid.org/0000-0001-7629-8702"},"institutions":[{"id":"https://openalex.org/I176601375","display_name":"Khalifa University of Science and Technology","ror":"https://ror.org/05hffr360","country_code":"AE","type":"education","lineage":["https://openalex.org/I176601375"]}],"countries":["AE"],"is_corresponding":true,"raw_author_name":"M. Sami Zitouni","raw_affiliation_strings":["Khalifa University, Abu Dhabi, UAE"],"affiliations":[{"raw_affiliation_string":"Khalifa University, Abu Dhabi, UAE","institution_ids":["https://openalex.org/I176601375"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000335994","display_name":"Andrzej \u015aluzek","orcid":"https://orcid.org/0000-0003-4148-2600"},"institutions":[{"id":"https://openalex.org/I170230895","display_name":"Warsaw University of Life Sciences","ror":"https://ror.org/05srvzs48","country_code":"PL","type":"education","lineage":["https://openalex.org/I170230895"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Andrzej Sluzek","raw_affiliation_strings":["Warsaw University of Life Sciences - SGGW, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Warsaw University of Life Sciences - SGGW, Warsaw, Poland","institution_ids":["https://openalex.org/I170230895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5038092547"],"corresponding_institution_ids":["https://openalex.org/I176601375"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.18696682,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"58","issue":null,"first_page":"1150","last_page":"1155"},"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":0.9983999729156494,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.991100013256073,"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/discriminative-model","display_name":"Discriminative model","score":0.8358983993530273},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.7546205520629883},{"id":"https://openalex.org/keywords/crowd-psychology","display_name":"Crowd psychology","score":0.6732690930366516},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6598577499389648},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6209514141082764},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6011765599250793},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5767831802368164},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5656877160072327},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4579514265060425},{"id":"https://openalex.org/keywords/space","display_name":"Space (punctuation)","score":0.42180484533309937},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41195985674858093},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.11019068956375122}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8358983993530273},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.7546205520629883},{"id":"https://openalex.org/C44042526","wikidata":"https://www.wikidata.org/wiki/Q1355183","display_name":"Crowd psychology","level":2,"score":0.6732690930366516},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6598577499389648},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6209514141082764},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6011765599250793},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5767831802368164},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5656877160072327},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4579514265060425},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.42180484533309937},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41195985674858093},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.11019068956375122},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icarcv50220.2020.9305476","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icarcv50220.2020.9305476","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1683537077","https://openalex.org/W1918003469","https://openalex.org/W1962468782","https://openalex.org/W1975697292","https://openalex.org/W1990369155","https://openalex.org/W1991668342","https://openalex.org/W2011128100","https://openalex.org/W2013640163","https://openalex.org/W2042015300","https://openalex.org/W2042127251","https://openalex.org/W2052761906","https://openalex.org/W2054554150","https://openalex.org/W2065994824","https://openalex.org/W2079023123","https://openalex.org/W2094614786","https://openalex.org/W2098824039","https://openalex.org/W2108404684","https://openalex.org/W2125095437","https://openalex.org/W2125556102","https://openalex.org/W2152813046","https://openalex.org/W2156856133","https://openalex.org/W2158958397","https://openalex.org/W2161969291","https://openalex.org/W2164489414","https://openalex.org/W2191835017","https://openalex.org/W2209193152","https://openalex.org/W2217690257","https://openalex.org/W2345188676","https://openalex.org/W2518965973","https://openalex.org/W2529436972","https://openalex.org/W2570343428","https://openalex.org/W2600200270","https://openalex.org/W2620860631","https://openalex.org/W2770385712","https://openalex.org/W2943957188","https://openalex.org/W2964297864","https://openalex.org/W2985585302","https://openalex.org/W2989694788","https://openalex.org/W6639968031","https://openalex.org/W6678706630","https://openalex.org/W6683119190"],"related_works":["https://openalex.org/W2165912799","https://openalex.org/W2965546495","https://openalex.org/W2735662278","https://openalex.org/W2382615723","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W4311804456","https://openalex.org/W3103844505","https://openalex.org/W1987484445","https://openalex.org/W2623658258"],"abstract_inverted_index":{"The":[0,26,35,63,116],"paper":[1],"proposes":[2],"mid-level":[3],"features":[4,36,64,88],"for":[5],"socio-cognitive":[6],"classification":[7,27],"of":[8,18,93,109,123],"crowd":[9,48],"behavior":[10],"in":[11,15,112,135],"public":[12],"spaces,":[13],"particularly":[14],"the":[16,72,84,87,107,113,124,128,136,141,144],"context":[17],"monitoring":[19],"social":[20,110],"interactions":[21,111],"during,":[22],"e.g.,":[23],"pandemic":[24],"restrictions.":[25],"method":[28],"follows":[29],"a":[30,91,100],"recently":[31],"proposed":[32],"categorization":[33],"[37].":[34],"are":[37,65,97,130],"built":[38],"using":[39],"statistics":[40],"obtained":[41,70],"from":[42,71,79,90],"detection":[43],"and":[44,52,58],"tracking":[45],"results":[46,129],"forc":[47],"components,":[49],"i.e.":[50],"individuals":[51],"their":[53],"groups":[54],"(any":[55],"typical":[56],"detectors":[57],"trackers":[59],"can":[60],"be":[61],"used).":[62],"defined":[66],"by":[67],"static":[68],"(if":[69,77],"current":[73],"frame)":[74],"or":[75],"dynamic":[76],"derived":[78],"consecutive":[80],"frames)":[81],"parameters":[82],"characterizing":[83],"crowd.":[85],"Subsequently,":[86],"extracted":[89],"number":[92],"most":[94],"recent":[95],"frames":[96],"fed":[98],"into":[99],"fully-connected":[101],"shallow":[102],"neural":[103],"network":[104],"to":[105],"identify":[106],"type":[108],"monitored":[114],"space.":[115],"experimental":[117],"feasibility":[118],"study":[119],"shows":[120],"encouraging":[121],"performances":[122],"approach.":[125],"In":[126],"particular,":[127],"far":[131],"more":[132],"discriminative":[133],"than":[134],"other":[137],"solution":[138],"(which,":[139],"at":[140],"moment,":[142],"is":[143],"only":[145],"publicly":[146],"known":[147],"benchmark).":[148]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
