隨著科技的發展，電腦視覺已廣泛應用到許多日常生活中，例如人臉辨識和障礙物閃避。本論文則使用立體視覺之原理對目標物做距離量測，首先會先對兩台攝影機做校正得到相機的內外參數，再利用兩台攝影機在相同場景下取得兩張影像，但由於利用人工調整的兩台攝影機不一定會在同一水平面上，因此我們會先利用相機的內外參數去校正影像使兩張影像的對應點是水平的，最後我們會利用Oriented FAST and Rotated BRIEF (ORB)特徵點演算法，計算兩張影像的對應點和視差並求出攝影機與目標物的距離。本論文主要提出兩種距離量測系統，一個系統是對兩張影像手動選取目標物做立體匹配得到距離，另一個系統則是利用Histogram of Oriented Gradient (HOG)特徵點結合Support Vector Machine (SVM)去訓練行人偵測分類器，利用此分類器在兩張影像自動偵測行人，再用ORB特徵點去對行人的區域去做立體匹配得到距離。另外我們也重新修正原本的距離公式並與原公式去做比較。而本文的成果可以為車輛避撞系統對行人的識別與警示。

With the development of technology, computer vision is now widely applied in daily life, in applications such as face recognition and obstacle avoidance. In this thesis, we use the principles of stereo vision to measure distance. Firstly, we calibrate two cameras to obtain the intrinsic and extrinsic parameters, and then we use the two cameras to capture images of the same scene. However, the two cameras are not necessarily on the same horizontal level, and thus we would use intrinsic and extrinsic parameters to rectify the images so that the corresponding points of two images are on the same horizontal level. After that we use the Oriented FAST and Rotated BRIEF (ORB) feature algorithm to obtain the corresponding points and calculate disparity and distance. This thesis proposes two distance measurement systems. One manually select objects from two images. The other system combines the concept of a Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) to train a pedestrian classifier. We use the classifier to detect pedestrians in two images automatically, and then we use the ORB features to calculate the disparity and distance in the pedestrian region. In addition, we revise the original distance formula to compare with the existing distance formula. The results of this research can be used to avoid collisions with pedestrians.

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