{"id":"https://openalex.org/W3007708315","doi":"https://doi.org/10.1109/bigdata47090.2019.9006063","title":"Training-free Monocular 3D Event Detection System for Traffic Surveillance","display_name":"Training-free Monocular 3D Event Detection System for Traffic Surveillance","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007708315","doi":"https://doi.org/10.1109/bigdata47090.2019.9006063","mag":"3007708315"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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/A5056705600","display_name":"Lijun Yu","orcid":"https://orcid.org/0000-0003-0645-1657"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Lijun Yu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100659434","display_name":"Peng Chen","orcid":"https://orcid.org/0000-0001-6122-0574"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Chen","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045007670","display_name":"Wenhe Liu","orcid":"https://orcid.org/0000-0003-4679-2958"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenhe Liu","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011488839","display_name":"Guoliang Kang","orcid":"https://orcid.org/0000-0003-1978-2025"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guoliang Kang","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103099928","display_name":"Alexander G. Hauptmann","orcid":"https://orcid.org/0000-0003-2123-0684"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Alexander G. Hauptmann","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, US"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, US","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5056705600"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":1.1232,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.82873808,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"161","issue":null,"first_page":"3838","last_page":"3843"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9998000264167786,"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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998000264167786,"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":0.9994999766349792,"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/computer-science","display_name":"Computer science","score":0.7313792705535889},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6637195944786072},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.6526125073432922},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.604702353477478},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5442140698432922},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4918370246887207},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.4842395484447479},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.4632282257080078},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.42338305711746216},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35617414116859436},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.33834779262542725}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7313792705535889},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6637195944786072},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.6526125073432922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.604702353477478},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5442140698432922},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4918370246887207},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.4842395484447479},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.4632282257080078},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.42338305711746216},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35617414116859436},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.33834779262542725},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"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/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006063","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006063","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","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":17,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1992989752","https://openalex.org/W2056341306","https://openalex.org/W2065429801","https://openalex.org/W2154665055","https://openalex.org/W2222512263","https://openalex.org/W2565639579","https://openalex.org/W2590234360","https://openalex.org/W2603203130","https://openalex.org/W2897254539","https://openalex.org/W2900787926","https://openalex.org/W2903541953","https://openalex.org/W2911458006","https://openalex.org/W2963150697","https://openalex.org/W2963795951","https://openalex.org/W3044021461","https://openalex.org/W6639102338"],"related_works":["https://openalex.org/W2789522126","https://openalex.org/W2066693961","https://openalex.org/W2368363778","https://openalex.org/W122584421","https://openalex.org/W4244295168","https://openalex.org/W230091440","https://openalex.org/W2753351751","https://openalex.org/W3185180338","https://openalex.org/W4394050964","https://openalex.org/W2551249631"],"abstract_inverted_index":{"We":[0],"focus":[1],"on":[2,132,162],"the":[3,14,64,85,107,110,129,133,147,150,156,169],"problem":[4],"of":[5,16,39,68,159,171,178],"detecting":[6],"traffic":[7,21,60,101],"events":[8,130],"in":[9,45,80,88],"a":[10,36,93],"surveillance":[11,69,165],"scenario,":[12],"including":[13],"detection":[15,25,98],"both":[17],"vehicle":[18],"actions":[19],"and":[20,30,55,76,114,149],"collisions.":[22],"Existing":[23],"event":[24,97],"systems":[26],"are":[27,181],"mostly":[28],"learning-based":[29],"have":[31],"achieved":[32],"convincing":[33],"performance":[34],"when":[35],"large":[37],"amount":[38],"training":[40,51],"data":[41,52],"is":[42,53,71,144],"available.":[43],"However,":[44],"real-world":[46,164],"scenarios,":[47],"collecting":[48],"sufficient":[49],"labeled":[50],"expensive":[54],"sometimes":[56],"impossible":[57],"(e.g.":[58],"for":[59,100],"collision":[61],"detection).":[62],"Moreover,":[63],"conventional":[65],"2D":[66],"representation":[67],"views":[70,79],"easily":[72],"affected":[73],"by":[74],"occlusions":[75,148],"different":[77],"camera":[78],"nature.":[81],"To":[82],"deal":[83],"with":[84],"aforementioned":[86],"problems,":[87],"this":[89],"paper,":[90],"we":[91,120],"propose":[92],"training-free":[94],"monocular":[95],"3D":[96,111],"system":[99,104,143,180],"surveillance.":[102],"Our":[103],"firstly":[105],"projects":[106],"vehicles":[108],"into":[109],"Euclidean":[112],"space":[113],"estimates":[115],"their":[116],"kinematic":[117,134],"states.":[118],"Then":[119],"develop":[121],"multiple":[122],"simple":[123],"yet":[124],"effective":[125],"ways":[126],"to":[127,146],"identify":[128],"based":[131],"patterns,":[135],"which":[136,167],"need":[137],"no":[138],"further":[139],"training.":[140],"Consequently,":[141],"our":[142,160,172,179],"robust":[145],"viewpoint":[151],"changes.":[152],"Exclusive":[153],"experiments":[154],"report":[155],"superior":[157],"result":[158],"method":[161],"large-scale":[163],"datasets,":[166],"validates":[168],"effectiveness":[170],"proposed":[173],"system.":[174],"The":[175],"demonstration":[176],"videos":[177],"available":[182],"online":[183],"<sup":[184],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[185],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[186],".":[187]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":7},{"year":2020,"cited_by_count":4}],"updated_date":"2026-04-28T14:05:53.105641","created_date":"2025-10-10T00:00:00"}
