Real-Time Image Segmentation and Rule-based Reasoning for Vehicle Head Light Detection on a Moving Vehicle

B.-F. Wu, Y.-L. Chen, Y.-H. Chen, C.-J. Chen, and C.-T. Lin (Taiwan)


Vehicle detection, night scene, image segmentation, multilevel thresholding, autonomous vehicles


This study proposes a vehicle detection system for identifying the vehicles by locating their headlights and rear-lights in the nighttime road environment. The proposed system comprises of two stages for detecting the vehicles in front of the camera-assisted car. The first stage is a fast automatic multilevel thresholding, which separates the bright objects from the grabbed nighttime road scene images. This proposed automatic multilevel thresholding approach provide the robustness and adaptability for the system to operate on various illuminated conditions at night. Then the extracted bright objects are processed by the second stage – the proposed knowledge-based connected component analysis procedure, to identify the vehicles by locating their vehicle lights, and estimate the distance between the camera-assisted car and the detected vehicles. Experimental results demonstrate the feasibility and effectiveness of the proposed approach on vehicle detection at night.

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