Outlier Detection on Vehicle Trajectories in Santiago, Chile using Unsupervised Deep Learning

dc.contributor.authorSoria, Richard
dc.contributor.authorCaro Saldivia, Luis
dc.contributor.authorPeralta Márquez, Billy
dc.contributor.authorIEEE
dc.date2019
dc.date.accessioned2021-04-30T16:32:56Z
dc.date.available2021-04-30T16:32:56Z
dc.description.abstractCurrently, a large amount of data is generated in the telemetry sector of vehicles in cities due to the continuous monitoring of vehicle trajectories through multiple sensors. Some trajectories generated by the sensors turn out not to correspond to the reality due to artefacts such as buildings, bridges or sensor failures, and where due to their large volume a manual verification of their correctness is not feasible. In this work, we propose the use of deep neural network models without supervision based on stacked autoencoders to detect atypical trajectories in vehicles within Santiago, Chile. The results show that the proposed model shows that it is able to detect that the atypical vehicle paths detected are at least 85% correct when considering the validation of a human expert. As future work, we propose to incorporate the use of LSTM networks in our model.
dc.identifier.citation2019 38TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC),Vol.,,2019
dc.identifier.urihttp://repositoriodigital.uct.cl/handle/10925/2936
dc.language.isoen
dc.publisherIEEE
dc.source2019 38TH INTERNATIONAL CONFERENCE OF THE CHILEAN COMPUTER SCIENCE SOCIETY (SCCC)
dc.subject.englishOutlier Detection
dc.subject.englishVehicle Trajectory
dc.subject.englishDeep Learning
dc.titleOutlier Detection on Vehicle Trajectories in Santiago, Chile using Unsupervised Deep Learning
dc.typeMeeting
uct.catalogadorWOS
uct.indizacionISTP
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