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Two Stage Intelligent Automotive System to Detect and Classify a Traffic Light

With the commercialization of autonomous vehicles and advanced automotive systems, the problem of detecting and classifying objects from a computer system has become a crucial issue. The objective of this paper is to detect and classify a traffic light from a scenery image. Different from most of other works, the proposed system will do this in two stages. The first one explores the feature extraction process in a set of images containing one or more traffic lights. This process focuses on detecting true positive areas where the traffic lights are and it is done through a six substage classifier using an ensemble of weak learners. The second stage obtains the input from the first stage, extracts its features and classifies them. The output of the second stage is a general classification for the color of the best traffic light found in the image scenery. The proposed recognition system is a robust, reliable and intelligent system with high rates of predictability.