{"id":"https://openalex.org/W2166989413","doi":"https://doi.org/10.1109/icassp.2009.4959874","title":"Grouping motion trajectories","display_name":"Grouping motion trajectories","publication_year":2009,"publication_date":"2009-04-01","ids":{"openalex":"https://openalex.org/W2166989413","doi":"https://doi.org/10.1109/icassp.2009.4959874","mag":"2166989413"},"language":"en","primary_location":{"id":"doi:10.1109/icassp.2009.4959874","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2009.4959874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"},"type":"conference-paper","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/A5072450987","display_name":"Samuel Pachoud","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Samuel Pachoud","raw_affiliation_strings":["School of Electronic Engineering and Computer Science, Queen Mary College, University of London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering and Computer Science, Queen Mary College, University of London, UK","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022534924","display_name":"Emilio Maggio","orcid":null},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Emilio Maggio","raw_affiliation_strings":["School of Electronic Engineering and Computer Science, Queen Mary College, University of London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering and Computer Science, Queen Mary College, University of London, UK","institution_ids":["https://openalex.org/I166337079"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004087827","display_name":"Andrea Cavallaro","orcid":"https://orcid.org/0000-0001-5086-7858"},"institutions":[{"id":"https://openalex.org/I166337079","display_name":"Queen Mary University of London","ror":"https://ror.org/026zzn846","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I166337079"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Andrea Cavallaro","raw_affiliation_strings":["School of Electronic Engineering and Computer Science, Queen Mary College, University of London, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electronic Engineering and Computer Science, Queen Mary College, University of London, UK","institution_ids":["https://openalex.org/I166337079"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I166337079"],"apc_list":null,"apc_paid":null,"fwci":0.8045,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74291202,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"b 36","issue":null,"first_page":"1477","last_page":"1480"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9993000030517578,"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":0.9993000030517578,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9855999946594238,"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/T12391","display_name":"Artificial Immune Systems Applications","score":0.9753000140190125,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.7000147700309753},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6330397129058838},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6248031854629517},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6142480373382568},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5070095062255859},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.506065309047699},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.5017318725585938},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.49461957812309265},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.4910593032836914},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.48164811730384827},{"id":"https://openalex.org/keywords/bayesian-information-criterion","display_name":"Bayesian information criterion","score":0.45706242322921753},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.430271714925766},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4277617931365967},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.4225269854068756},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4191245436668396},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.37071138620376587},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3198047876358032},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3045837879180908},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2667473554611206},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15396136045455933}],"concepts":[{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.7000147700309753},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6330397129058838},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6248031854629517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6142480373382568},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5070095062255859},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.506065309047699},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.5017318725585938},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.49461957812309265},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.4910593032836914},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.48164811730384827},{"id":"https://openalex.org/C168136583","wikidata":"https://www.wikidata.org/wiki/Q1988242","display_name":"Bayesian information criterion","level":2,"score":0.45706242322921753},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.430271714925766},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4277617931365967},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.4225269854068756},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4191245436668396},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.37071138620376587},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3198047876358032},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3045837879180908},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2667473554611206},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15396136045455933},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp.2009.4959874","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp.2009.4959874","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Acoustics, Speech and Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W124020047","https://openalex.org/W1723319196","https://openalex.org/W1998871699","https://openalex.org/W2008810234","https://openalex.org/W2026140901","https://openalex.org/W2047216628","https://openalex.org/W2076078388","https://openalex.org/W2112081648","https://openalex.org/W2140017549","https://openalex.org/W2148238513","https://openalex.org/W2151263703","https://openalex.org/W2164223054","https://openalex.org/W2168175751","https://openalex.org/W4238024369","https://openalex.org/W4241521373","https://openalex.org/W6605095686","https://openalex.org/W6680527857","https://openalex.org/W6682028543","https://openalex.org/W6683941694"],"related_works":["https://openalex.org/W1566728629","https://openalex.org/W3184936773","https://openalex.org/W3131759240","https://openalex.org/W2166980079","https://openalex.org/W106647055","https://openalex.org/W2085386807","https://openalex.org/W2330255023","https://openalex.org/W4311388919","https://openalex.org/W1992295166","https://openalex.org/W2143508933"],"abstract_inverted_index":{"We":[0,63],"present":[1],"a":[2,21,37],"method":[3,81],"to":[4,96],"group":[5],"trajectories":[6,16],"of":[7,86],"moving":[8],"objects":[9],"extracted":[10],"from":[11],"real-world":[12,72],"surveillance":[13],"videos.":[14],"The":[15,47],"are":[17,34],"first":[18],"mapped":[19],"into":[20],"low":[22],"dimensionality":[23],"feature":[24],"space":[25],"generated":[26],"through":[27],"linear":[28],"regression.":[29],"Next":[30],"the":[31,65,78,92,97],"regression":[32,87],"coefficients":[33],"clustered":[35],"by":[36,42],"Gaussian":[38],"mixture":[39,85],"model":[40,48],"initialized":[41],"K-means":[43,83],"for":[44],"improved":[45],"efficiency.":[46],"selection":[49],"problem":[50],"is":[51],"solved":[52],"with":[53,60],"Bayesian":[54],"information":[55],"criterion":[56],"that":[57,77],"penalizes":[58],"models":[59],"high":[61],"complexity.":[62],"demonstrate":[64],"proposed":[66,79],"approach":[67],"on":[68],"both":[69],"synthetic":[70],"and":[71,84],"scenes.":[73],"Experimental":[74],"results":[75],"show":[76],"clustering":[80],"outperforms":[82],"models,":[88],"while":[89],"also":[90],"reducing":[91],"computational":[93],"complexity":[94],"compared":[95],"latter.":[98]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
