近幾年人們對於環境的安全性更加的重視，許多專家入續投入研究人工智慧型的監控式系統。在計算機視覺的領域裡，虹膜，面貌，語音，指紋，手寫辨識，和步伐等常被用來當作辨識人身份的特徵。在這篇論文當中，我們使用步伐來當作辨識的特徵，因為此特徵的粹取並不需要和人近距離的接觸，可以和人保持一定的距離，而且並不需要太精密的攝影設備。每個人走路的姿勢，手腳的擺幅，和力道都不ㄧ樣，所以我們利用它來做身份辨識。在此我們結合了步伐的動態和靜態資訊去辨識人步行的路徑和身份。首先我們利用擺幅距離的週期性去估計每一段影像序列的週期並將每段週期獨立分割出來。對於每段週期，我們利用邏輯運算取得靜態資訊，而動態資訊我們利用分析移動向量的直方圖統計來取得。將所取得的資訊轉換到低維度的空
間，在此空間的特徵向量代表此人。假設不同類別的特徵向量群為獨立的高斯分佈，然後我們使用鑑別函式去決定此人的步行路徑。當測試的特徵向量進來，我們拿它來和事先建立好的資料庫做比對，用距離鄰近法則來判定此人的身份，最後我們利用臨界值來選擇切換要用靜態或動態資訊的排序來當作辨識的結果。實驗結果證實我們所提出的系統架構可以有效的克服拿公事包遮蔽身體的問題，並可達到滿高的辨識率。

In this thesis, we combine the dynamic and static information extracted from gait to identify the walking human object. First we utilize the periodicity of swing distances to estimate the gait period for each gait sequence and divide them to sub-cycles. For each gait cycle, we extract the static information by proceeding intersecting operation and dynamic information by analyzing the statistic histogram of motion vectors. The extracted information is transformed into low dimensional embedding space by dimensionality reduction process. The low-dimensional feature vector is used to represent the subject. Then, we use a set of discriminant functions to determine the decision regions for normal data distribution, and then we can recognize the human walking path. Given a test feature vector, the nearest neighbor classifier is applied to compare with the feature vectors established from a gait database for subject identification. The proposed algorithm is evaluated on the CASIA gait database, and the experimental results demonstrate that own system achieves a high recognition rate.

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