Versatile visual detection techniques for advanced safety vehicles

Xin Liang Shen, Din Chang Tseng, Chun Wei Lin, Tony Hu, Regulus Liou

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

Abstract

A monocular visual detection system including twelve detection functions is proposed to assist the road driving for safety. In the lane-mark detection, the lateral inhibition property of human vision system is simulated to improve the detector to satisfy all different weather conditions and to avoid the influence of windshield wiper. The preceding vehicles are detected based on the underneath shadow, left/right borders, and multiple templates of vehicles; then verified by the ratio of lane and vehicle widths, symmetry, and gray-level variance of vehicle regions. The preceding-vehicle distance is estimated based on a single camera with only known focus length and setting height. The pitch and yaw angles of the camera are estimated from the proposed method. The experimental results show that the proposed methods are stable and effective for safety detection in various weather conditions: sunny, misty, dusty, cloudy, rainy day, and night. The average vehicle detected rate is 94.5 %.

Original languageEnglish
Title of host publicationProceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008
Pages397-403
Number of pages7
StatePublished - 2008
Event2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008 - Las Vegas, NV, United States
Duration: 14 Jul 200817 Jul 2008

Publication series

NameProceedings of the 2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008

Conference

Conference2008 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2008
Country/TerritoryUnited States
CityLas Vegas, NV
Period14/07/0817/07/08

Keywords

  • Advance safety vehicle
  • Computer vision
  • Lane detection & classification
  • Vehicle detection & distance estimation

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