基於多特徵和物體追蹤技術的行人偵測技術

隨著行車交通安全越來越受到重視，行人偵測技術在電腦視覺中是一項非常重要的課題。本論文的目標在於搭建一個實時的行人偵測技術平臺，目標在於能夠使用普通個人電腦的CPU達到實時處理視頻數據，從中找出行人的效果。本論文首先回顧了國立台灣大學資訊工程系數位相機與電腦視覺實驗室對行人檢測技術的研究，並且回顧了相應的主流的行人偵測技術，通過實驗比較並且提升了相應的行人檢測率。文章採用了方向梯度直方圖特徵(Histogram of Oriented Gradient,簡稱HOG), 局部二值特徵(Local Binary Pattern,簡稱LBP), 以及顏色相似特徵直方圖(Color histogram of Self-Similarity of low-level features，簡稱CSS),並且使用一定的分類器組合，從而降低行人檢測錯誤率。本文在降低行人檢測錯誤率的同時，為提升行人檢測速度，採用了光學分析的方法並且假設相機為針孔模型並且對向地面呈一定角度，從而推導得搜尋區域和目標高度行人之間的關係。為了達到實時的行人檢測效果，除了檢測程式之外，本文還採用了快速的Lucas-Kanade光流計算方法, 在連續的兩張照片中，可以快速計算所檢測到的行人的運動向量，大大降低了搜索行人所造成的時間消耗。同時，針對HOG特徵計算，本文使用了快速獲得不同尺度圖像特徵方法，大量節省計算積分特徵圖像所消耗的時間。通過採取以上的措施，最終搭建了一個實時檢測行人的平臺。

關鍵字

行人偵測；物體追蹤；機器學習

並列摘要

Pedestrian detection has become a very important topic in computer vision, due to the higher attention paid to driving security. This thesis aims at building a platform for real-time pedestrian detection, using Central Processing Unit (CPU) of personal computers to process the video stream data for pedestrian detection. This thesis first reviews some research works of pedestrian detection done by Digital Camera and Computer Vision Laboratory, Department of Computer Science and Information Engineering, National Taiwan University and also mainstream detection method of finding pedestrians in an image. We significantly reduce the miss detection rate by some combination of features of Histogram of Oriented Gradient (HOG), Local Binary Patterns (LBP) and Color histogram of Self-Similarity of low-level features (CSS). Not only to reduce the miss detection rate, our program also increased the detecting speed to achieve the real-time performance. This thesis analyzes the relationship between search regions and the height of pedestrians by assuming driving recorders are placed at a specific angle towards the ground and the camera to be a pin-hole model. Besides pedestrian detection, we also apply the Lucas-Kanade method for computing the optical flow to track the pedestrians who have already been detected between two consecutive images and significantly reduced the time consumption. Meanwhile, for HOG features, we also apply a method to infer features under different scales to save the computation time of integral feature images. By using the methods above, we build a platform for real-time pedestrian detection.

並列關鍵字

Pedestrian Detection ； Object Tracking ； Machine Learning

參考文獻

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國際替代計量

基於多特徵和物體追蹤技術的行人偵測技術

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