This thesis proposes a novel multiple traffic light recognition system based on videos captured by a monocular camera. Advanced driver assistance system (ADAS) and autonomous driving system (ADS) are becoming increasingly important to help drivers maneuvering vehicles and increase the vehicle and road safety in modern life. Traffic light recognition system is a significant part of ADAS and ADS, which can detect traffic light on the road and recognize different types of traffic lights to provide useful signal information for drivers. The proposed method can be applied to real complex environment only based on a monocular camera and is tested in real-world scenarios. This system consists of three parts: multiple traffic light detection, multi-target tracking and state classification. For the first step, a supervised machine learning method, support vector machine (SVM) with two integral features - histogram of oriented gradients (HOG) and histogram of CIELAB color space (HCIELAB), are used to detect traffic lights in the captured image. Then, a new multi-target tracking algorithm is presented to improve the accuracy of detection, reduce the number of false alarm and missing targets, by means of nearest neighbor data association, motion model analysis and Lucas-Kanade optical flow tracking and the region of interest (ROI) prediction. Finally, a SVM-based and a convolution neural network (CNN) based classifiers are introduced to classify the state of traffic lights, that provides the stop, go, warning, straight and turn information. Various experiments have been conducted to demonstrate the practicability of the proposed method. Both GPU-based and CPU-based programming can run real-time on the real street environment. / Thesis / Master of Applied Science (MASc)

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