An effective approach for forward vehicle detection with tracking

Chi, Fu-Hsiang; Huo, Chih-Li; Yu, Yu-Hsaing; Sun, Tsung-Ying

doi:10.1109/gcce.2012.6379952

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Object detection is to distinguish a class of objects which have large intraclass variability such as faces, pedestrians, and vehicles from cluttered background [ 19 , 20 ]. Many vision-based vehicle detection systems have been designed only for highway traffic where lanes are usually well marked and built with slowly changing curvature [ 2 , 21 , 22 ]. Whereas vehicles appear quite separated from background in highway traffic environment, vehicles have to be distinguished from more cluttered background in urban traffic environment [ 23 ].…”

Section: Backgroundsmentioning

confidence: 99%

Robust Range Estimation with a Monocular Camera for Vision-Based Forward Collision Warning System

Park

Hwang

2014

The Scientific World Journal

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We propose a range estimation method for vision-based forward collision warning systems with a monocular camera. To solve the problem of variation of camera pitch angle due to vehicle motion and road inclination, the proposed method estimates virtual horizon from size and position of vehicles in captured image at run-time. The proposed method provides robust results even when road inclination varies continuously on hilly roads or lane markings are not seen on crowded roads. For experiments, a vision-based forward collision warning system has been implemented and the proposed method is evaluated with video clips recorded in highway and urban traffic environments. Virtual horizons estimated by the proposed method are compared with horizons manually identified, and estimated ranges are compared with measured ranges. Experimental results confirm that the proposed method provides robust results both in highway and in urban traffic environments.

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