智慧型交通系統(ITS) 是近幾年來相當熱門的研究方向。主要是自動監控十字路口的交通情況, 這樣的系統可以降低人力的需求, 還可以自動預測可能發生車禍的情行, 並警告駕駛人或者行人, 使道路使用者對於危險的狀況提早做出反應,讓發生的車禍案件次數下降, 保障行人及駕駛者生命安全, 並保持道路的順暢, 降低國家不必要的社會成本付出。
在我們的系統中, 我們首先利用前景和背景分離的方法, 以背景相減法, 萃取出我們所需要的移動物體資訊, 並過濾掉雜訊, 得到乾淨的分離移動物體圖。接著利用類似平面的場景, 給每一個移動物體一個編號, 而每一個編號都有所屬移動物體的特徵, 比如說: 位置、面積和速度...... 等等。接著追蹤移動物體在十字路口的行進軌跡, 並在追蹤的過程中,利用最小平方解的方法, 求出二次曲線或者一次曲線的模擬路線圖, 利用未來的軌跡路線,去觀察車子和行人之間的關係, 判斷是否可能發生車禍。在追蹤的過程中, 並分類移動物體是車子、摩托車或者是行人, 利用我們預測到的軌跡, 可以對十字路口的交通情況作一種程度上的了解, 比如說: 有車子正在轉彎或者是有行人正在通過斑馬線、往左行或者往右行的車子和摩拖車有多少...... 等等。

Among the most impotant research in Intelligent Transportation Systems (ITS) is the development of systems that automatically monitor traffic flow at intersections.
Such systems would be useful both in reducing the workload of human operators and in warning driver and pedestrian of dangerous situations. The might be able to reduce the number of accidents and protect safe of driver and pedestrian.
In our system, we employ algorithm of moving objects segmentation to extract moving objects information that give a new object ID to those object piexls. Each
ID have own features, such as position, area and motion vector. Then we track each ID and employ least-square solution and linear or quadratics equation to request
future trajectory. We can distinguish that moving objects are car or motorcycle or pedestrian and understand situations of traffic. When a car to make a turn and pedestrian to pass through intersection can predict whether they might lead to accident.

第一章 介紹
1.1 研究目的
1.2 論文架構
第二章 相關研究
2.1 移動物體的分離與追蹤
2.1.1 移動物體的分離
2.1.2 移動物體的追蹤
2.2 最小平方解
2.3 RGB 色彩模型
2.4 相關論文發表
2.4.1 Abnormal Incident Detection System Employing Image
Processing Technology(西元1999年)
2.4.2 Evaluation of Auto Incident Recording System
(西元2005年)
2.4.3 Analysis and Query of Person-vehicle Interactions in Homography Domain(西元2006年)
2.4.4 A Traffic Accident Recording and Reporting Model at
Intersections(西元2007年)
2.4.5 Multiple Objects Segmentation and Tracking Algorithm
for Intersection Monitoring(西元2008年)
第三章 演算法流程
3.1 系統流程圖
3.2 前景分離及相連區域偵測
3.3 特徵分析和追蹤
3.4 未來軌跡預測
3.5 警告行人
第四章 實驗結果
第五章 結論和未來工作

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