{"id":"https://openalex.org/W2031352624","doi":"https://doi.org/10.1145/1099396.1099404","title":"Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients","display_name":"Classifying spatiotemporal object trajectories using unsupervised learning of basis function coefficients","publication_year":2005,"publication_date":"2005-11-11","ids":{"openalex":"https://openalex.org/W2031352624","doi":"https://doi.org/10.1145/1099396.1099404","mag":"2031352624"},"language":"en","primary_location":{"id":"doi:10.1145/1099396.1099404","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1099396.1099404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the third ACM international workshop on Video surveillance &amp; sensor networks","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/A5015169296","display_name":"Shehzad Khalid","orcid":"https://orcid.org/0000-0003-0899-7354"},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shehzad Khalid","raw_affiliation_strings":["University of Manchester, Manchester, United Kingdom","University of Manchester, MANCHESTER, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Manchester, Manchester, United Kingdom","institution_ids":["https://openalex.org/I28407311"]},{"raw_affiliation_string":"University of Manchester, MANCHESTER, United Kingdom","institution_ids":["https://openalex.org/I28407311"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111910242","display_name":"Andrew Naftel","orcid":null},"institutions":[{"id":"https://openalex.org/I28407311","display_name":"University of Manchester","ror":"https://ror.org/027m9bs27","country_code":"GB","type":"education","lineage":["https://openalex.org/I28407311"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Andrew Naftel","raw_affiliation_strings":["University of Manchester, Manchester, United Kingdom","University of Manchester, MANCHESTER, United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Manchester, Manchester, United Kingdom","institution_ids":["https://openalex.org/I28407311"]},{"raw_affiliation_string":"University of Manchester, MANCHESTER, United Kingdom","institution_ids":["https://openalex.org/I28407311"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I28407311"],"apc_list":null,"apc_paid":null,"fwci":2.4645,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.89930139,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"45","last_page":"52"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9993000030517578,"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":0.9993000030517578,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9993000030517578,"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"}},{"id":"https://openalex.org/T11439","display_name":"Video Analysis and Summarization","score":0.9803000092506409,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7242315411567688},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.658854067325592},{"id":"https://openalex.org/keywords/mahalanobis-distance","display_name":"Mahalanobis distance","score":0.6149216294288635},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6055477857589722},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6003439426422119},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5675937533378601},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5161300897598267},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4830775558948517},{"id":"https://openalex.org/keywords/fourier-series","display_name":"Fourier series","score":0.4694642126560211},{"id":"https://openalex.org/keywords/basis-function","display_name":"Basis function","score":0.4267244338989258},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.27508559823036194}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7242315411567688},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.658854067325592},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.6149216294288635},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6055477857589722},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6003439426422119},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5675937533378601},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5161300897598267},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4830775558948517},{"id":"https://openalex.org/C207864730","wikidata":"https://www.wikidata.org/wiki/Q179467","display_name":"Fourier series","level":2,"score":0.4694642126560211},{"id":"https://openalex.org/C5917680","wikidata":"https://www.wikidata.org/wiki/Q2621825","display_name":"Basis function","level":2,"score":0.4267244338989258},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27508559823036194},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1145/1099396.1099404","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1099396.1099404","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the third ACM international workshop on Video surveillance &amp; sensor networks","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.461.8506","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.461.8506","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://pdf.aminer.org/000/368/784/motion_trajectory_learning_in_the_dft_coefficient_feature_space.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1501103983","https://openalex.org/W1679913846","https://openalex.org/W1723319196","https://openalex.org/W1853995153","https://openalex.org/W1993855803","https://openalex.org/W2003295980","https://openalex.org/W2006783944","https://openalex.org/W2026140901","https://openalex.org/W2042591571","https://openalex.org/W2055767715","https://openalex.org/W2076078388","https://openalex.org/W2096930266","https://openalex.org/W2102283219","https://openalex.org/W2106068408","https://openalex.org/W2109328551","https://openalex.org/W2109553605","https://openalex.org/W2112440119","https://openalex.org/W2118572719","https://openalex.org/W2124088136","https://openalex.org/W2128061541","https://openalex.org/W2135098562","https://openalex.org/W2143888015","https://openalex.org/W2147880780","https://openalex.org/W2148939213","https://openalex.org/W2151138777","https://openalex.org/W2155351433","https://openalex.org/W2156507712","https://openalex.org/W2157200118","https://openalex.org/W2159860664","https://openalex.org/W2163336863","https://openalex.org/W2164773496","https://openalex.org/W2170728027","https://openalex.org/W2600339340","https://openalex.org/W4296580867","https://openalex.org/W6661122342","https://openalex.org/W6676184124"],"related_works":["https://openalex.org/W4382795578","https://openalex.org/W2355463328","https://openalex.org/W2402648945","https://openalex.org/W1431147547","https://openalex.org/W2771741613","https://openalex.org/W2055761197","https://openalex.org/W2053213469","https://openalex.org/W2157426608","https://openalex.org/W2420560403","https://openalex.org/W2778199868"],"abstract_inverted_index":{"This":[0],"paper":[1],"proposes":[2],"a":[3,48,84],"novel":[4],"technique":[5],"for":[6,26],"clustering":[7,53],"and":[8,39,130],"classification":[9],"of":[10,29,67,73,139],"object":[11,69,92,132],"trajectory-based":[12],"video":[13,148],"motion":[14,144],"clips":[15],"using":[16,41],"spatiotemporal":[17],"functional":[18],"approximations.":[19],"A":[20],"Mahalanobis":[21],"classifier":[22],"is":[23,54],"then":[24,55],"used":[25,78],"the":[27,42,59,74,118,137],"detection":[28],"anomalous":[30],"trajectories.":[31],"Motion":[32],"trajectories":[33,93,99],"are":[34,77,122,151],"considered":[35],"as":[36,79],"time":[37],"series":[38],"modeled":[40],"leading":[43],"Fourier":[44,50,60],"coefficients":[45,72],"obtained":[46],"by":[47],"Discrete":[49],"Transform.":[51],"Trajectory":[52],"carried":[56],"out":[57],"in":[58,94,100,147],"coefficient":[61],"feature":[62,81],"space":[63],"to":[64,83,104,116,135,143],"discover":[65],"patterns":[66],"similar":[68],"motions.":[70],"The":[71],"basis":[75],"functions":[76],"input":[80],"vectors":[82,115],"Self-Organising":[85],"Map":[86],"which":[87],"can":[88],"learn":[89],"similarities":[90],"between":[91],"an":[95],"unsupervised":[96],"manner.":[97],"Encoding":[98],"this":[101],"way":[102],"leads":[103],"efficiency":[105],"gains":[106],"over":[107],"existing":[108],"approaches":[109],"that":[110],"use":[111],"discrete":[112],"point-based":[113],"flow":[114],"represent":[117],"whole":[119],"trajectory.":[120],"Experiments":[121],"performed":[123],"on":[124],"two":[125],"different":[126],"datasets":[127],"--":[128],"synthetic":[129],"pedestrian":[131],"tracking":[133],"-":[134],"demonstrate":[136],"effectiveness":[138],"our":[140],"approach.":[141],"Applications":[142],"data":[145],"mining":[146],"surveillance":[149],"databases":[150],"envisaged.":[152]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":3},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":6},{"year":2012,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
