如今車輛數正以一個驚人的數字攀升中，許多協助駕駛的智慧型行車系統也如雨後春筍般的被開發出來，路牌偵測與辨識正是其中很重要的一塊，此系統不僅能通知駕駛道路情況，更能使其免於記憶眾多繁瑣的路牌種類。於本篇論文中，我們提出了一個可以快速偵測並辨識路牌的系統。我們提出的系統利用HSI色彩空間來過濾大部份非目標區塊，並利用梯度長方圖 (Histogram of Oriented Gradient)來判斷區塊的形狀(包含圓形、三角形、方形、八角形)，最後再利用GentleBoost分類器及rotation, scale, translation-invariant (RST-invariant) 的樣板比對方式來分類它是屬於哪一種路牌。在我們的演算法的偵測步驟，梯度長方圖資訊足夠讓我們解決一些形變、大小、及部分遮蔽的問題；在辨識部分，我們利用車牌的色彩部分來訓練GentleBoost分類器並配合非顏色部分的RST-invariant樣板比對方式。此系統的優點是能快速的偵測路牌並能有效的利用色彩部份結合圖形資訊來辨識路牌。

Nowadays, the number of vehicles is growing rapidly, and more and more intelligent transportation systems are developed for assisting drivers. Road sign detection and recognition is extremely important for safe and careful driving, this system can not only inform the driver about the condition of the roadway but also support the driver during the tedious task of remembering the large number of road signs. In this thesis, we propose a fast road sign detection and recognition system. This system takes advantage of the HSI color space to filter most of the false alarms. Histogram of Oriented Gradient (HOG) is then used for the shape detection (including circular, triangular, rectangular, and octagonal signs), finally, the candidate blobs that pass through the shape detection is recognized by a GentleBoost detector and rotation, scale, translation-invariant (RST-invariant) template matching. In detection step of our algorithm, deformation, scale, and partial occlusion problems can be solved by utilizing HOG information; for recognition step, Color information is used for training GentleBoost detector to ensure the accuracy of the system; and the achromatic part of the candidates are matched to the templates by RST-invariant template matching. The main advantage of this system is that it can detect and recognize road signs efficiently and accurately.

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