以二維物體影像比對與三維電腦視覺分析作自動車航行之研究以及 其於室內安全巡邏之應用

Abstract

本論文基於電腦視覺技術提出了一套室內安全巡邏自動車系統，採用一台載有PTZ網路攝影機及具無線遙控功能的小型自動車作為實驗平台。本論文首先提出了一個易於使用的學習方法，供系統作學習巡邏環境之運用，其中包括：巡邏路線、地板顏色、監控物品，以及自動車相對於監控物品之位置。接著，本論文提出一個自動車安全巡邏的方法使自動車能進行安全監控以及障礙物自動閃避的工作。自動車會根據學習到的路徑作巡邏，並利用本論文所提出的簡化式SIFT(Scale Invariant Feature Transform)方法進行物品監控。該方法從含有監控物品的影像中抽取特徵點，並與學習所得版本進行比對，再藉由霍夫轉換找出兩版本間的仿射轉換，據以測定自動車相對於物品之位置，以及修正自動車航行路徑的偏移。此外，本論文也利用地板的代表顏色來偵測障礙物，讓自動車適應於花色地板的環境，並整合目標導向路徑追隨之策略，使車子能自動閃避障礙物。最後我們利用一實際的室內環境來測驗本論文所提出的系統，結果自動車能順利自動修正路徑以及監控物品，顯示出本論文所提方法的完整性以及可行性。

A vision-based vehicle system for security patrolling in indoor environments using an autonomous vehicle is proposed. A small vehicle with wireless control and a web camera which has the capabilities of panning, tilting, and zooming is used as a test bed. At first, an easy-to-use learning technique is proposed, which has the capability of extracting specific features, including navigation path, floor color, monitored object, and vehicle location with respect to monitored objects. Next, a security patrolling method by vehicle navigation with obstacle avoidance and security monitoring capabilities is proposed. The vehicle navigates according to the node data of the path map which is created in the learning phase and monitors concerned objects by a simplified scale-invariant feature transform (simplified-SIFT) algorithm proposed in this study. Accordingly, we can extract the features of each monitored object from acquired images and match them with the corresponding learned data by the Hough transform. Furthermore, a vehicle location estimation technique for path correction utilizing the monitored object matching result is proposed. In addition, techniques for obstacle avoidance are also proposed, which can be used to find the clusters of floor colors, detect obstacles in environments with various floor colors, and integrate a technique of goal-directed minimum path following to guide the vehicle to avoid obstacles. Good experimental results show the flexibility and feasibility of the proposed methods for the application of security patrolling in indoor environments.