在這資訊科技突飛猛進的時代，隨著電腦的運算能力的日益精進，使得影像處理的相關應用蓬勃的發展。影像辨識於行人檢出應用於駕駛輔助系統與監控、保全方面，一直是各方重要的研究課題。
本研究探討了變化檢出(change detection)，小邊線元(ridgelets)描述，以及靜態影像的行人檢出的問題。利用變化檢出以偵測行人的方式可避免行人與背景間的混雜。不過，其方法的應用僅限於對固定背景下的連續影像。本研究提出利用鏈碼長度來濾除行人背景所造成的干擾，並由輸入行人邊緣影像訓練行人樣板，再選取樣板的特徵點以便進行比對。
本研究取72張行人影像作訓練，利用訓練後所得特徵分別對訓練內、外的影像進行測試，並針對其結果探討行人檢出所遭遇的問題。

With the rapid progress in information technology, computer is becoming more and more powerful, applications of digital image processing and machine vision are getting popular. The application of the image recognition to pedestrian detection in the security system and the advanced driver assistance system is an important and active research area.
In this thesis, change detection, ridgelets for the representation of pedestrian with edges, and pedestrian detection on static images are discussed. The change detection method is often used to segment the moving objects out of the scene for pedestrian detection, and must be in the same background. This paper presents a way to detect pedestrian on images in different scenes by limiting chain code length used to reduce the deterioration due to the background noise in the image. In pedestrian detection, we obtain edge templates from input images and extract the feature points by training edge templates. It detects pedestrian in different scenes by the feature points matching.
The research employs 72 pedestrian images for training and uses the extracted features to test. The problem in pedestrian detection is then discussed based on the experimental results.

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