行人偵測是電腦視覺中的一個重要議題，包含行動機器人、監控系統與行車安全的幾個應用中；尤其是行動機器人的行人偵測。這是一個富有挑戰性的題目，因為部分的行動機器人機身較矮或架設的攝影機擷取角度過低，而無法得到完整的人形影像。本論文提出了一個行人人腿偵測系統來取代整個行人的偵測。我們系統的最終目的是在一個行動機器人所錄製的影片中去偵測與追蹤行人的人腿。我們的系統只使用了一支簡單的攝影機即可完成行人人腿的偵測，且執行速度快、準確率高。
在人腿偵測的程序中，我們結合一些知名的演算法來做影像前處理，並評估了兩種不同分類器的偵測效能，分別為：倒傳遞類神經網路(Back-Propagation Neural Network)和支持向量機(Support Vector Machine)；使用此兩種不同的分類器來偵測行人人腿的區域。
在人腿追蹤的程序中，我們利用粒子濾波器(Particle filter)技術來動態追蹤人腿。我們以邊緣及顏色等資訊當作特徵，使在追蹤的過程中盡可能讓背景的影像達到最低。
根據實驗結果顯示, 我們的系統可以快速偵測與追蹤多位行人的人腿；而且,可以在複雜環境下獲得很高的偵測率。我們所提方法的人腿偵測率在室內環境下超過 95%.，而在室外的環境下，仍有91.3%以上的表現；我們實驗了人腿走動的八個方向，平均偵測率能達到90%。 另外, 人腿偵測的效率達到每秒9 到 10 張畫面. 這結果表示，此方法是可行且有效的偵測人腿。

Pedestrian detection is a key issue in computer vision, with several applications including robotics, surveillance and automotive safety, especially in autonomous mobile robots. However, it is difficult to detect pedestrians because the height of some autonomous mobile robots is not tall enough and the angle of the camera on the robot is limited so that it is hard to capture complete image of pedestrian. In this thesis, we propose a pedestrian legs detection system to replace complete pedestrian body detection. The ultimate goal of the system is detecting and tracking pedestrian legs in video sequences recorded by a moving autonomous mobile robot. In the proposed system, we only use single webcam in our practice yet we can achieve fast and accurate detection.
In the procedure of pedestrian legs detection, we combined many well-known methods to do image pre-processing and we evaluate two machine learning methods: back-propagation neural network (BP-NN) and support vector machine (SVM) to detection pedestrian legs.
In the procedure of pedestrian legs tracking, we propose an improved particle filter to dynamically locate a human leg. We further utilize the edge and color information as the features to make the influence of the background as little as possible.
After experiments, our system can quickly detect multi-pedestrian legs. Moreover, high accuracy detection rate can be accomplished even under cluttering backgrounds. The detect rate of pedestrian legs is more than 95.0% in indoor background and still reaches 91.3% in outdoor background. Moreover, the detection rate of eight different type of leg’s direction can also achieve 90%. On the other hand, the detection performance is 9 to 10 frames per second. The results reveal that the proposed method is feasible and efficient for detecting pedestrian legs.

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