{"id":"https://openalex.org/W2921190655","doi":"https://doi.org/10.5220/0007307600270038","title":"Vehicle Activity Recognition using Mapped QTC Trajectories","display_name":"Vehicle Activity Recognition using Mapped QTC Trajectories","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2921190655","doi":"https://doi.org/10.5220/0007307600270038","mag":"2921190655"},"language":"en","primary_location":{"id":"doi:10.5220/0007307600270038","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007307600270038","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.5220/0007307600270038","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012867846","display_name":"Alaa AlZoubi","orcid":"https://orcid.org/0000-0003-1167-170X"},"institutions":[{"id":"https://openalex.org/I92864154","display_name":"University of Buckingham","ror":"https://ror.org/03kd28f18","country_code":"GB","type":"education","lineage":["https://openalex.org/I92864154"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Alaa AlZoubi","raw_affiliation_strings":["School of Computing, The University of Buckingham, Buckingham and U.K., --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"School of Computing, The University of Buckingham, Buckingham and U.K., --- Select a Country ---","institution_ids":["https://openalex.org/I92864154"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109309152","display_name":"David Nam","orcid":null},"institutions":[{"id":"https://openalex.org/I933054736","display_name":"Defence Academy of the United Kingdom","ror":"https://ror.org/03myaza48","country_code":"GB","type":"education","lineage":["https://openalex.org/I1306956679","https://openalex.org/I2802373619","https://openalex.org/I933054736"]},{"id":"https://openalex.org/I82284825","display_name":"Cranfield University","ror":"https://ror.org/05cncd958","country_code":"GB","type":"education","lineage":["https://openalex.org/I82284825"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"David Nam","raw_affiliation_strings":["Centre for Electronic Warfare, Information and Cyber, Cranfield University, Defence Academy of the UK and U.K., --- Select a Country ---"],"affiliations":[{"raw_affiliation_string":"Centre for Electronic Warfare, Information and Cyber, Cranfield University, Defence Academy of the UK and U.K., --- Select a Country ---","institution_ids":["https://openalex.org/I933054736","https://openalex.org/I82284825"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5012867846"],"corresponding_institution_ids":["https://openalex.org/I92864154"],"apc_list":null,"apc_paid":null,"fwci":0.1022,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.40806256,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"27","last_page":"38"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.9998000264167786,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9998000264167786,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9995999932289124,"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/trajectory","display_name":"Trajectory","score":0.779044508934021},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7516867518424988},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.660419225692749},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6603618860244751},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5075494050979614},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.49933624267578125},{"id":"https://openalex.org/keywords/obstacle","display_name":"Obstacle","score":0.47855526208877563},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.46515923738479614},{"id":"https://openalex.org/keywords/activity-recognition","display_name":"Activity recognition","score":0.45902472734451294},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4464522898197174},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.44556039571762085},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4368836283683777},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4196714162826538},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4129721522331238},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11942970752716064}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.779044508934021},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7516867518424988},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.660419225692749},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6603618860244751},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5075494050979614},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.49933624267578125},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.47855526208877563},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.46515923738479614},{"id":"https://openalex.org/C121687571","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Activity recognition","level":2,"score":0.45902472734451294},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4464522898197174},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.44556039571762085},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4368836283683777},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4196714162826538},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4129721522331238},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11942970752716064},{"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","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},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5220/0007307600270038","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007307600270038","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"},{"id":"pmh:oai:bear.buckingham.ac.uk:389","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4377196562","display_name":"Buckingham E-Archive of Research (University of Buckingham)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I92864154","host_organization_name":"University of Buckingham","host_organization_lineage":["https://openalex.org/I92864154"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Book Section"}],"best_oa_location":{"id":"doi:10.5220/0007307600270038","is_oa":true,"landing_page_url":"https://doi.org/10.5220/0007307600270038","pdf_url":null,"source":null,"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1525638028","https://openalex.org/W1741349288","https://openalex.org/W1967321478","https://openalex.org/W2000143015","https://openalex.org/W2085121383","https://openalex.org/W2095705004","https://openalex.org/W2097117768","https://openalex.org/W2107105977","https://openalex.org/W2108598243","https://openalex.org/W2117539524","https://openalex.org/W2127784071","https://openalex.org/W2137431914","https://openalex.org/W2161381512","https://openalex.org/W2161986999","https://openalex.org/W2163605009","https://openalex.org/W2212870984","https://openalex.org/W2312681412","https://openalex.org/W2463627759","https://openalex.org/W2565516711","https://openalex.org/W2567297805","https://openalex.org/W2612933602","https://openalex.org/W2618757996","https://openalex.org/W2737860972","https://openalex.org/W2883286874","https://openalex.org/W3101203783"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2794103424","https://openalex.org/W1996530509","https://openalex.org/W3028317537","https://openalex.org/W2389515972","https://openalex.org/W4245435724","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W2055301889","https://openalex.org/W103069296"],"abstract_inverted_index":{"The":[0],"automated":[1,139],"analysis":[2,140],"of":[3,45,54,128,141,153],"interacting":[4],"objects":[5],"or":[6],"vehicles":[7],"has":[8],"many":[9],"uses,":[10],"including":[11],"autonomous":[12],"driving":[13],"and
\\nsecurity":[14],"surveillance.":[15],"In":[16],"this":[17,103],"paper":[18],"we":[19,81,149],"present":[20,150],"a":[21,52,86,151],"novel":[22],"method":[23,93,113],"for":[24,144],"vehicle":[25,142],"activity":[26,106],"recognition":[27,107],"using":[28,114],"Deep
\\nConvolutional":[29],"Neural":[30],"Network":[31],"(DCNN).":[32],"We":[33,57,109],"use":[34],"Qualitative":[35],"Trajectory":[36],"Calculus":[37],"(QTC)":[38],"to":[39,61],"represent":[40],"the":[41,63,70,78,98,105,145],"relative
\\nmotion":[42],"between":[43],"pair":[44],"vehicles,":[46],"and":[47,90,101,118],"encode":[48],"their":[49],"interactions":[50],"as":[51],"trajectory":[53,64],"QTC":[55,75,83],"states.":[56],"then
\\nuse":[58],"one-hot":[59],"vectors":[60],"map":[62],"into":[65,85],"2D":[66],"matrix":[67],"which":[68],"conserves":[69],"essential":[71],"position":[72],"information
\\nof":[73],"each":[74],"state":[76],"in":[77,138],"sequence.":[79],"Specifically,":[80],"project":[82],"sequences":[84],"two":[87,115],"dimensional":[88],"image
\\ntexture,":[89],"subsequently":[91],"our":[92,112],"adapt":[94],"layers":[95],"trained":[96],"on":[97],"ImageNet":[99],"dataset":[100,152],"transfer":[102],"knowledge
\\nto":[104],"task.":[108],"have":[110],"evaluated":[111],"different":[116],"datasets,":[117],"shown
\\nthat":[119],"it":[120],"out-performs":[121],"state-of-the-art":[122],"methods,":[123],"achieving":[124],"an":[125,136],"error":[126],"rate":[127],"no":[129],"more":[130],"than":[131],"1.16%.":[132],"Our":[133],"motivation
\\noriginates":[134],"from":[135,157],"interest":[137],"movement":[143],"collision":[146],"avoidance":[147],"application,
\\nand":[148],"vehicle-obstacle":[154],"interaction,":[155],"collected":[156],"simulator-based":[158],"experiments.":[159]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}