本論文所探討的議題為許多汽車的車主在開車門時未注意到後方的機車或汽車，而發生車門與車輛碰撞的意外，每年都造成了許多死傷。因此本研究提出一套影像處理方法，應用於白天且天氣良好的情況偵測移動物件，一旦移動物件進到危險的開門範圍，就會傳遞訊號給車子的系統，迅速將車門鎖上；其影像處理過程分別是影像前處理、移動物件偵測、改良ViBe演算法、物件分類和偵測危險。影像前處理使用中值濾波器處理原始影像，讓雜訊降低；移動物件偵測提出以ViBe的演算法取代傳統的連續影像相減法和背景模型法，解決影像輪廓缺失的問題；改良ViBe演算法解決了拖影區的問題，並且加快了背景更新的速度；物件分類的部分，使用了支持向量機來快速獲取結果，危險偵測使用像素和實際距離的轉換定義出危險碰撞範圍。本研究採用不同背景所提出的1367筆移動物件資料進行測試，檢測單張影像的速度僅需0.15秒~0.2秒，成功在危險碰撞範圍前偵測出移動物件的比率為100%。再利用其中的800筆汽機車資料進行分類，得到正確分類率為97.86%，汽車和機車的正確分類率分別為94.62%和97.51%。

In our research, we focus on the accident when people get out off car, but they didn’t notice the coming object, such as cars and motorcycles. As a result, the object crash into the car door and both of drivers will injure badly. This kind of accident happen every day and cause lots of people die. Therefore, we build a system of image processing which can apply to detect objects. Once any object in the dangerous zone, the system will submit signal to car system and lock car door. The process of system can divide into image preprocessing, moving object detection, ViBe algorithm, modified ViBe algorithm and object classifying. Image preprocessing part will use median filter to original image and make noises less. moving object detection part will use ViBe algorithm instead of frame differencing and background model. This algorithm has good ability about object contour detecting. Modified ViBe algorithm solves the ghost area problem and speeds up the period of background updating. Object classifying uses the support vector machine to obtain the final result.
This research has 1367 data which are based on eight different backgrounds. All the backgrounds have different street, different car and different motor. Due to the result, it only takes 0.2 seconds to process one frame. The accuracy rate of detecting object is up to 100%. The accuracy rate of classifying object is about 97.86%. The accuracy rate of classifying car and motor are 94.62% and 97.51% individually.

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