A novel driving pattern recognition and status monitoring system

Jiann Der Lee*, Jiann Der Li, Li Chang Liu, Chi Ming Chen

*Corresponding author for this work

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

29 Scopus citations

Abstract

This paper describes a novel driving pattern recognition and status monitoring system based on the orientation information. Two fixed cameras are used to capture the driver's image and the front-road image. The driver's sight line and the driving lane path are found from these 2 captured images and are mapped into a global coordinate. Two correlation coefficients among the driver's sight line, the driving lane path and the car heading direction are calculated in the global coordinate to monitor the driving status such as a safe driving status, a risky driving status and a dangerous driving status. The correlation coefficients between the lane path and car heading direction in a fixed period are analyzed and recognized as one of 4 driving patterns by HMM. Four driving patterns including the driving in a straight lane, the driving in a curve lane, the driving of changing lanes, and the driving of making a turn are able to be recognized so far.

Original languageEnglish
Title of host publicationAdvances in Image and Video Technology - First Pacific Rim Symposium, PSIVT 2006, Proceedings
PublisherSpringer Verlag
Pages504-512
Number of pages9
ISBN (Print)354068297X, 9783540682974
DOIs
StatePublished - 2006
Event1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006 - Hsinchu, Taiwan
Duration: 10 12 200613 12 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4319 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st Pacific Rim Symposium on Image and Video Technology, PSIVT 2006
Country/TerritoryTaiwan
CityHsinchu
Period10/12/0613/12/06

Keywords

  • Ada-boosted
  • Driving event
  • Driving safety monitoring
  • HMM
  • Intelligent transport
  • Shape-context

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