Fast car crash detection in video

Vicente Enrique Machaca Arceda, Elian Laura Riveros

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

In this work, we aim to detect car crash accidents in video. We propose a three-stage framework: The first one is a car detection method using convolutional neural networks, in this case, we used the net You Only Look Once (YOLO); the second stage is a tracker in order to focus each car; then the final stage for each car we use the Violent Flow (ViF) descriptor with a Support Vector Machine (SVM) in order to detect the car crashes. Our proposal is almost in real time with just 0.5 seconds of delay and also we got a 89% accuracy detecting car crashes.

Original languageEnglish
Title of host publicationProceedings - 2018 44th Latin American Computing Conference, CLEI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages632-637
Number of pages6
ISBN (Electronic)9781728104379
DOIs
StatePublished - Oct 2018
Event44th Latin American Computing Conference, CLEI 2018 - Sao Paulo, Brazil
Duration: 1 Oct 20185 Oct 2018

Publication series

NameProceedings - 2018 44th Latin American Computing Conference, CLEI 2018

Conference

Conference44th Latin American Computing Conference, CLEI 2018
Country/TerritoryBrazil
CitySao Paulo
Period1/10/185/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Car Crash
  • Convolutional Networks
  • Deep Learning
  • SVM
  • ViF

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