視覺式車輛特徵分析之研究

Abstract

由於交通監控系統的普及化，偵測、追蹤並且辨識道路影片中的移動物體，逐漸成為一項重要的研究議題。由於大部份的事故是由汽車所引起的，而且車輛的外型特徵相較於車牌號碼，更不易被偽造或者隱藏，因此本論文將建構一個智慧型交通監控系統，能夠在各種攝影機角度、各種反光影響下，辨識車輛的外型特徵，包括顏色、大小與款式。為了克服光影的變化，排除車窗、車燈等無關車色像素的影響，本論文提出一個三階段車體切割演算法，分別針對亮色系、暗色系與彩色系的車子，設計不同策略切割車體，車輛的顏色僅考慮車體內部像素，因此可以得到更精準的車輛主顏色，更進一步得到更正確的車色分類結果。為了快速地估計車輛大小以及旋轉角度，本系統提出對稱中心演算法，用來搜尋車頭的對稱中心，並計算此中心到最近邊緣的距離，此距離與車輛高度以及寬度的比例，分別與車輛的大小與角度呈現單調函數，因此車輛的大小與角度可藉由比例的反函數來求得。為了在各種角度下更精準的辨識車款，我們提出鏡射形變技術，此技術可將在各種角度下拍攝到的車子都調整成標準的正面、側面或者背面，接著選擇與測試車相同角度，並同樣經過形變處理的樣本車來比對。由於鏡射形變技術可有效地排除角度估計誤差以對稱中心搜尋誤差，因此能比傳統以角度估計為基礎的車款辨識方法提供更高的正確率。

With the rapid growth of surveillance equipments, detecting, tracking, and recognizing moving objects in roadway videos is currently a popular issue. Because most accidents are caused by cars and the appearance features of cars are hard to be hidden or counterfeited like license plate, this research develops an intelligent traffic monitoring system which identifies appearance features, such as sizes, models, and colors under varying camera viewpoint and light reflections. Due to the effect of non-homogeneous light reflection, the improper foreground pixels, such as the windshield, or lamps influence the extraction of color type. A tri-states car body segmentation algorithm is proposed in this dissertation. Different strategies are designed for bright, dark, and colored cars, and only the pixels belonging to the car body are considered for color classification. Therefore, a purer car color can be extracted and a more correct color type can be classified. To rapidly estimate the size and pose of a car, a symmetric center detection algorithm is proposed. The algorithm searches the symmetric center on the head (or rear) of a car and computes the distance between the center and the closest boundary as half of the head width. Two aspect ratios: car height to head (or rear) width and head (or rear) width to car width, are designed to identify the car size and car pose. To recognize car model across varying poses, a mirror morphing scheme is proposed. The scheme is able to transform cars with varying poses into a typical (front, rear, or side) view. Then a template car with the same pose with the tested car is selected and matched against the tested car. Because the mirror morphing scheme effectively reduces center bias and estimation error of tested and template cars, higher recognition rate can be anticipated. Finally, the experiments show that the proposed system is superior to conventional approaches for classifying colors, sizes, and models of cars.