支援向量機(Support Vector Machines)是一種新熱門的機器學習演算法，近年來廣泛的應用在文字識別，影像分類，生物資訊等領域上，在影像的辨識與效率上都有很好的表現。 本研究使用支援向量機建構行人辨識系統，利用行人輪廓線條特徵來當作訓練樣本，幫助在視線不清或無法辨識人臉特徵的情況下辨識行人。 本論文以固定式攝影機拍攝畫面，透過背景相減法擷取出移動的影像，在移動影像上定位出行人的頭部影像位置，並且利用物體大小與移動方向預估移動向量，使用最佳化搜尋區域(best-area-search)劃分法與物體色彩特徵比對法，匹配影像前後期移動的繼承關係，持續追蹤軌跡。最後抽取出行人特徵，並經由SVM分類器驗證行人影像。經由實驗證明，本系統可達到快速且有效率的辨識結果。 In this thesis, a real-time pedestrian detection method is presented which can be employed in outdoor environments. The system still has to successfully detect pedestrian under the environments of blurred face features. In our approach, the moving silhouettes of a walking figure is firstly detected by using the technique of background subtraction, and the blobs boundaries are located with the help of head candidate. The trajectory of the moving person is generated by best-area-search and the people activities are analyzed using color feature correlation of object. To achieve the goal of effective and real-time detection, the technique of Support Vector Machines (SVM) is adopted, which works well especially in object prediction and classification. The vertical edge features extracted from body, legs, and head are fed to the SVM as the features. Experiments were conducted on both binary edge images and gray-level images. The experimental results demonstrate that our proposed method is feasible and effective in pedestrian detection.