{"id":"https://openalex.org/W2798712664","doi":"https://doi.org/10.1109/mapr.2018.8337520","title":"Measuring Crowd Collectiveness with Trajectory Smoothing","display_name":"Measuring Crowd Collectiveness with Trajectory Smoothing","publication_year":2018,"publication_date":"2018-04-01","ids":{"openalex":"https://openalex.org/W2798712664","doi":"https://doi.org/10.1109/mapr.2018.8337520","mag":"2798712664"},"language":"en","primary_location":{"id":"doi:10.1109/mapr.2018.8337520","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mapr.2018.8337520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","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/A5016368466","display_name":"Quy Nguyen Pham Phu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quy Nguyen Pham Phu","raw_affiliation_strings":["Multimedia Communications Laboratory, VNU-HCM"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimedia Communications Laboratory, VNU-HCM","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006269866","display_name":"Vy Nguyen","orcid":"https://orcid.org/0000-0003-3436-3662"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vy Nguyen","raw_affiliation_strings":["Multimedia Communications Laboratory, VNU-HCM"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimedia Communications Laboratory, VNU-HCM","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103126474","display_name":"Tien Do","orcid":"https://orcid.org/0000-0002-5381-3414"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tien Do","raw_affiliation_strings":["Multimedia Communications Laboratory, VNU-HCM"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Multimedia Communications Laboratory, VNU-HCM","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081841048","display_name":"Thanh Duc Ngo","orcid":"https://orcid.org/0000-0001-6882-0070"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Thanh Duc Ngo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.03905234,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9951000213623047,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9925000071525574,"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/smoothing","display_name":"Smoothing","score":0.8744223713874817},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7581558227539062},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7261121869087219},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6270566582679749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5890412330627441},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5716659426689148},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5370093584060669},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.4806317389011383},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4725475013256073},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.438036173582077},{"id":"https://openalex.org/keywords/collective-motion","display_name":"Collective motion","score":0.43798524141311646},{"id":"https://openalex.org/keywords/crowd-simulation","display_name":"Crowd simulation","score":0.43262091279029846},{"id":"https://openalex.org/keywords/collective-behavior","display_name":"Collective behavior","score":0.4290130138397217},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41355395317077637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33200451731681824},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.2646559774875641},{"id":"https://openalex.org/keywords/crowds","display_name":"Crowds","score":0.18297800421714783},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1023428738117218},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10002204775810242}],"concepts":[{"id":"https://openalex.org/C3770464","wikidata":"https://www.wikidata.org/wiki/Q775963","display_name":"Smoothing","level":2,"score":0.8744223713874817},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7581558227539062},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7261121869087219},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6270566582679749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5890412330627441},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5716659426689148},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5370093584060669},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.4806317389011383},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4725475013256073},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.438036173582077},{"id":"https://openalex.org/C2780499737","wikidata":"https://www.wikidata.org/wiki/Q24755076","display_name":"Collective motion","level":2,"score":0.43798524141311646},{"id":"https://openalex.org/C45617602","wikidata":"https://www.wikidata.org/wiki/Q465266","display_name":"Crowd simulation","level":3,"score":0.43262091279029846},{"id":"https://openalex.org/C100339178","wikidata":"https://www.wikidata.org/wiki/Q2548752","display_name":"Collective behavior","level":2,"score":0.4290130138397217},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41355395317077637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33200451731681824},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2646559774875641},{"id":"https://openalex.org/C2777852691","wikidata":"https://www.wikidata.org/wiki/Q13430821","display_name":"Crowds","level":2,"score":0.18297800421714783},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1023428738117218},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10002204775810242},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mapr.2018.8337520","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mapr.2018.8337520","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 1st International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.47999998927116394}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W47624021","https://openalex.org/W631375206","https://openalex.org/W1922838137","https://openalex.org/W1981724556","https://openalex.org/W2015410655","https://openalex.org/W2036536917","https://openalex.org/W2040395913","https://openalex.org/W2042015300","https://openalex.org/W2062941309","https://openalex.org/W2067467244","https://openalex.org/W2096606660","https://openalex.org/W2097490641","https://openalex.org/W2101668106","https://openalex.org/W2109389234","https://openalex.org/W2112164016","https://openalex.org/W2121839066","https://openalex.org/W2122361470","https://openalex.org/W2125095437","https://openalex.org/W2134080476","https://openalex.org/W2149021215","https://openalex.org/W2150312211","https://openalex.org/W2469107314","https://openalex.org/W2915276330","https://openalex.org/W2963423876","https://openalex.org/W3037683188","https://openalex.org/W3040777582"],"related_works":["https://openalex.org/W2591718056","https://openalex.org/W4388772684","https://openalex.org/W1555506539","https://openalex.org/W4361292541","https://openalex.org/W2953929844","https://openalex.org/W2956815954","https://openalex.org/W4395030664","https://openalex.org/W4390489708","https://openalex.org/W3118258528","https://openalex.org/W2279927153"],"abstract_inverted_index":{"Analyzing":[0],"videos":[1],"of":[2,23,36,48,51,59,75,80,104],"crowd":[3,17],"scenes":[4],"plays":[5],"an":[6,95],"important":[7],"role":[8],"in":[9,63],"surveillance":[10],"systems.":[11],"It":[12],"is":[13,109],"essential":[14],"to":[15,43,72,97],"measure":[16],"collectiveness,":[18],"which":[19],"indicates":[20],"the":[21,34,45,73,99,131],"degree":[22],"individuals":[24,81],"acting":[25],"as":[26],"a":[27,119],"union.":[28],"State-of-the-art":[29],"approaches":[30,129],"mainly":[31],"relied":[32],"on":[33,118],"similarity":[35],"individual":[37,60],"behaviors":[38,54],"inside":[39],"and":[40,77],"outside":[41],"neighborhood":[42],"quantify":[44],"topological":[46],"structures":[47],"collective":[49],"manifolds":[50],"crowd.":[52],"Individual":[53],"were":[55],"estimated":[56],"using":[57,111],"velocity":[58],"trajectories":[61,67],"tracked":[62],"consecutive":[64],"frames.":[65],"However,":[66],"could":[68],"be":[69],"unstable":[70],"due":[71],"variations":[74],"speed":[76],"moving":[78],"direction":[79],"at":[82],"different":[83],"tracking":[84],"duration,":[85],"hence":[86],"inaccurate":[87],"collectiveness":[88],"measurement.":[89],"In":[90],"this":[91],"paper,":[92],"we":[93],"propose":[94],"approach":[96],"enhance":[98],"accuracy":[100],"by":[101],"stabilizing":[102],"motions":[103],"individuals.":[105],"The":[106],"stabilization":[107],"process":[108],"performed":[110],"smoothing":[112],"methods.":[113],"We":[114],"evaluated":[115],"our":[116],"methods":[117],"benchmark":[120],"dataset,":[121],"Collective":[122],"Motion.":[123],"Experimental":[124],"results":[125],"indicate":[126],"that":[127],"smoothing-based":[128],"outperform":[130],"original":[132],"state-of-the-art":[133],"approach.":[134]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}