An Image Based Overexposed Taillight Detection Method for Frontal Vehicle Detection in Night Vision

Abstract

To achieve the goal of frontal vehicle detection in night-driving condition, we propose an effective method to detect the red taillights of vehicles. The challenge is that the taillight images captured with automatic exposure typically are overexposed, which makes red color segmentation often erroneous. Instead of customizing the camera hardware to tackle this problem, we combine morphological and logical operations to extract the overexposed region in taillights, which leads to a much more reliable taillight detection scheme. Then, we develop a robust pairing process that clusters two taillight candidates into a pair that represents a vehicle. Several criteria are considered in the pairing process, including the similarities of area, shape, and height of a pair of lights. In addition, we include the temporal consistency criterion; that is, a pair of taillights should be continually detected for a certain duration of time. An energy function is used to aggregate these criteria together. Our experiments show that both the missing and false detection rates are lower than 1.5%.