{"id":"https://openalex.org/W2608584966","doi":"https://doi.org/10.1109/icpr.2016.7899948","title":"Human group activity recognition based on modelling moving regions interdependencies","display_name":"Human group activity recognition based on modelling moving regions interdependencies","publication_year":2016,"publication_date":"2016-12-01","ids":{"openalex":"https://openalex.org/W2608584966","doi":"https://doi.org/10.1109/icpr.2016.7899948","mag":"2608584966"},"language":"en","primary_location":{"id":"doi:10.1109/icpr.2016.7899948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.whiterose.ac.uk/id/eprint/127769/1/ICPR_GroupActivity.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5070915089","display_name":"Kyle Stephens","orcid":"https://orcid.org/0009-0008-0992-3449"},"institutions":[{"id":"https://openalex.org/I52099693","display_name":"University of York","ror":"https://ror.org/04m01e293","country_code":"GB","type":"education","lineage":["https://openalex.org/I52099693"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kyle Stephens","raw_affiliation_strings":["Department of Computer Science, University of York, York, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of York, York, UK","institution_ids":["https://openalex.org/I52099693"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002932489","display_name":"Adrian G. Bor\u015f","orcid":"https://orcid.org/0000-0001-7838-0021"},"institutions":[{"id":"https://openalex.org/I52099693","display_name":"University of York","ror":"https://ror.org/04m01e293","country_code":"GB","type":"education","lineage":["https://openalex.org/I52099693"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Adrian G. Bors","raw_affiliation_strings":["Department of Computer Science, University of York, York, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of York, York, UK","institution_ids":["https://openalex.org/I52099693"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I52099693"],"apc_list":null,"apc_paid":null,"fwci":1.2993,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88102161,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"32","issue":null,"first_page":"2115","last_page":"2120"},"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":0.9984999895095825,"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":0.9976999759674072,"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/discriminative-model","display_name":"Discriminative model","score":0.8260179758071899},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7070093154907227},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6992532014846802},{"id":"https://openalex.org/keywords/interdependence","display_name":"Interdependence","score":0.6739182472229004},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.6407910585403442},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6103371381759644},{"id":"https://openalex.org/keywords/kernel-density-estimation","display_name":"Kernel density estimation","score":0.5381482839584351},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5309357643127441},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5214875936508179},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.5141211152076721},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5097245573997498},{"id":"https://openalex.org/keywords/group","display_name":"Group (periodic table)","score":0.4852566719055176},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47406405210494995},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.47302958369255066},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36287862062454224},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13694167137145996},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.13517531752586365},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.08964389562606812}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.8260179758071899},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7070093154907227},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6992532014846802},{"id":"https://openalex.org/C185874996","wikidata":"https://www.wikidata.org/wiki/Q269699","display_name":"Interdependence","level":2,"score":0.6739182472229004},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.6407910585403442},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6103371381759644},{"id":"https://openalex.org/C71134354","wikidata":"https://www.wikidata.org/wiki/Q458825","display_name":"Kernel density estimation","level":3,"score":0.5381482839584351},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5309357643127441},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5214875936508179},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.5141211152076721},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5097245573997498},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.4852566719055176},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47406405210494995},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.47302958369255066},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36287862062454224},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13694167137145996},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.13517531752586365},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.08964389562606812},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icpr.2016.7899948","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icpr.2016.7899948","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 23rd International Conference on Pattern Recognition (ICPR)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.whiterose.ac.uk:127769","is_oa":true,"landing_page_url":"https://orcid.org/0000-0001-7838-0021>","pdf_url":"https://eprints.whiterose.ac.uk/id/eprint/127769/1/ICPR_GroupActivity.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"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":"Proceedings Paper"}],"best_oa_location":{"id":"pmh:oai:eprints.whiterose.ac.uk:127769","is_oa":true,"landing_page_url":"https://orcid.org/0000-0001-7838-0021>","pdf_url":"https://eprints.whiterose.ac.uk/id/eprint/127769/1/ICPR_GroupActivity.pdf","source":{"id":"https://openalex.org/S4306400854","display_name":"White Rose Research Online (University of Leeds, The University of Sheffield, University of York)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I2800616092","host_organization_name":"White Rose University Consortium","host_organization_lineage":["https://openalex.org/I2800616092"],"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":"Proceedings Paper"},"sustainable_development_goals":[{"score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320332972","display_name":"Defence Science and Technology Laboratory","ror":"https://ror.org/04jswqb94"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2608584966.pdf","grobid_xml":"https://content.openalex.org/works/W2608584966.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W1886885333","https://openalex.org/W1943626413","https://openalex.org/W1967456674","https://openalex.org/W1972696612","https://openalex.org/W1990332778","https://openalex.org/W2000143015","https://openalex.org/W2019080172","https://openalex.org/W2023048829","https://openalex.org/W2097745545","https://openalex.org/W2118704323","https://openalex.org/W2125105611","https://openalex.org/W2153464267","https://openalex.org/W2161969291","https://openalex.org/W2161986999","https://openalex.org/W2272213516","https://openalex.org/W2476035395","https://openalex.org/W2533503513","https://openalex.org/W2533739470","https://openalex.org/W3104298728","https://openalex.org/W6639381921","https://openalex.org/W6642206748","https://openalex.org/W6648012848","https://openalex.org/W6674887771","https://openalex.org/W6683802788"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3103844505","https://openalex.org/W2392100589","https://openalex.org/W259157601","https://openalex.org/W4205463238","https://openalex.org/W2512789322","https://openalex.org/W2761785940","https://openalex.org/W1482209366"],"abstract_inverted_index":{"In":[0],"this":[1],"research":[2],"study,":[3],"we":[4],"model":[5,47,74],"the":[6,30,38,42,50,58,64,72,109,124],"interdependency":[7],"of":[8,53,66,71,127],"actions":[9],"performed":[10],"by":[11,37,56],"people":[12],"in":[13,16,27,41,78,102],"a":[14,46,97],"group":[15,54],"order":[17],"to":[18,48],"identify":[19],"their":[20],"activity.":[21],"Unlike":[22,101],"single":[23],"human":[24,105],"activity":[25,33,55,106],"recognition,":[26],"interacting":[28],"groups":[29],"local":[31],"movement":[32],"is":[34,93,112],"usually":[35],"influenced":[36],"other":[39,103],"persons":[40],"group.":[43],"We":[44],"propose":[45],"describe":[49],"discriminative":[51],"characteristics":[52],"considering":[57],"relations":[59],"between":[60],"motion":[61],"flows":[62],"and":[63,80,114],"locations":[65],"moving":[67],"regions.":[68],"The":[69],"inputs":[70],"proposed":[73,110],"are":[75,85],"jointly":[76],"represented":[77],"time-space":[79],"time-movement":[81],"spaces.":[82],"These":[83],"spaces":[84],"modelled":[86],"using":[87],"Kernel":[88],"Density":[89],"Estimation":[90],"(KDE)":[91],"which":[92],"then":[94],"fed":[95],"into":[96],"machine":[98],"learning":[99],"classifier.":[100],"group-based":[104],"recognition":[107],"algorithms,":[108],"methodology":[111],"automatic":[113],"does":[115],"not":[116],"rely":[117],"on":[118,123],"any":[119],"pedestrian":[120],"detection":[121],"or":[122],"manual":[125],"annotation":[126],"tracks.":[128]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}