乐胖代购免代理版

{"id":"https://openalex.org/W3114777653","doi":"https://doi.org/10.1109/itsc45102.2020.9294251","title":"Social Pooling with Edge Convolutions on Local Connectivity Graphs for Human Trajectory Prediction in Crowded Scenes","display_name":"Social Pooling with Edge Convolutions on Local Connectivity Graphs for Human Trajectory Prediction in Crowded Scenes","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3114777653","doi":"https://doi.org/10.1109/itsc45102.2020.9294251","mag":"3114777653"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294251","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-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/A5025761381","display_name":"Athena Psalta","orcid":"https://orcid.org/0000-0002-7412-0529"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"A Psalta","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038983938","display_name":"V. Tsironis","orcid":"https://orcid.org/0000-0003-2592-2127"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"V Tsironis","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064461457","display_name":"\u039a\u03c9\u03bd\u03c3\u03c4\u03b1\u03bd\u03c4\u03af\u03bd\u03bf\u03c2 \u039a\u03b1\u03c1\u03ac\u03bd\u03c4\u03b6\u03b1\u03bb\u03bf\u03c2","orcid":"https://orcid.org/0000-0001-8730-6245"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"K Karantzalos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5016048550","display_name":"Ioanna Spyropoulou","orcid":"https://orcid.org/0000-0001-7801-3395"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"I Spyropoulou","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.207,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.253612,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":60,"max":69},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9989,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9989,"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":0.9988,"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":0.9986,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.86590064},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7050512},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.55734515},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.42536744}],"concepts":[{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.86590064},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.79342794},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7050512},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6524685},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.58327174},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.58273613},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.55734515},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.5489252},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5441254},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.53629225},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4271484},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.42536744},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.4151371},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26891464},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294251","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false}],"best_oa_location":null,"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":19,"referenced_works":["https://openalex.org/W1518259947","https://openalex.org/W1571268436","https://openalex.org/W1963649406","https://openalex.org/W1970206276","https://openalex.org/W2124609748","https://openalex.org/W2146183743","https://openalex.org/W2167052694","https://openalex.org/W2170345640","https://openalex.org/W2424778531","https://openalex.org/W2532516272","https://openalex.org/W2766836212","https://openalex.org/W2801667201","https://openalex.org/W2810931617","https://openalex.org/W2911273949","https://openalex.org/W2962687116","https://openalex.org/W2963001155","https://openalex.org/W2970989527","https://openalex.org/W2979750740","https://openalex.org/W2989796359"],"related_works":["https://openalex.org/W803346624","https://openalex.org/W4390975304","https://openalex.org/W4287804464","https://openalex.org/W3211292372","https://openalex.org/W3103989898","https://openalex.org/W3022252430","https://openalex.org/W2953234277","https://openalex.org/W2900413183","https://openalex.org/W2626256601","https://openalex.org/W147410782"],"abstract_inverted_index":{"Human":[0],"trajectory":[1,99,157],"prediction":[2,158],"is":[3,126],"a":[4,44,56,71,78,92,102,109,116,134,155,173],"quite":[5],"challenging":[6,182],"task":[7],"mainly":[8],"due":[9,82],"to":[10,61,77,83,97,128,169],"numerous":[11],"social":[12,136,145],"interactions":[13,42,132],"and":[14,21],"plausible":[15],"paths":[16],"in":[17,39,43,180],"complex":[18],"crowed":[19],"scenarios":[20],"varying":[22],"environments.":[23],"Data-driven":[24],"machine":[25],"learning":[26],"approaches":[27],"based":[28,114,160],"on":[29,55,101,115,122,133,161],"Recurrent":[30],"Neural":[31],"Networks":[32,164],"(RNNs)":[33],"have,":[34],"recently,":[35],"achieved":[36],"significant":[37,174],"results":[38],"modelling":[40],"human-human":[41,131],"scene.":[45],"However,":[46],"information-sharing":[47],"pooling":[48,94,100,146,178],"modules":[49,179],"across":[50],"RNN":[51],"Encoders":[52],"which":[53,125],"operate":[54],"local":[57,130],"spatial":[58],"context":[59],"fail":[60],"model":[62],"long-term":[63],"scene":[64],"level":[65],"correlations,":[66],"while":[67],"other":[68],"that":[69],"adopt":[70],"more":[72],"global":[73],"approach":[74],"are":[75],"restricted":[76],"rather":[79],"simplistic":[80],"formulations":[81],"high":[84],"computational":[85],"costs.":[86],"In":[87],"this":[88],"work,":[89],"we":[90,150],"introduce":[91],"novel":[93,110],"mechanism":[95],"designed":[96],"perform":[98],"higher":[103],"semantic":[104,135],"level.":[105],"We":[106],"have":[107,151],"developed":[108],"multi-layer":[111],"network":[112],"architecture":[113],"new":[117],"Edge":[118],"Convolutional":[119],"operator":[120],"acting":[121],"irregular":[123],"data":[124],"able":[127],"generalize":[129],"context.":[137],"To":[138],"assess":[139],"the":[140,143],"performance":[141],"of":[142],"proposed":[144],"with":[147],"edge":[148],"convolutions,":[149],"integrated":[152],"it":[153],"into":[154],"state-of-the-art":[156,177],"framework":[159],"Generative":[162],"Adversarial":[163],"(GANs).":[165],"Our":[166],"module":[167],"managed":[168],"overall":[170],"outperform,":[171],"by":[172],"margin,":[175],"several":[176],"real-world":[181],"benchmark":[183],"datasets.":[184]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W3114777653","counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2024-12-31T03:27:17.712187","created_date":"2021-01-05"}