近年來交通事故日益嚴重，已有許多廠商和研究機構為了行車的安全，陸續開發和研究即時安全車輛系統。車輛行駛中，駕駛本身與後照鏡的視野相當有限，因此無法清楚地觀察兩邊的盲點區域。當駕駛變換車道時，若來車處於盲點區域無法看到，就很容易發生碰撞的意外。因此為了改善這種情況，在本研究中，我們在車側的後照鏡下方裝設一台攝影機，發展電腦視覺偵測技術，協助駕駛偵測側邊盲點區域的來車。系統會即時偵測對本車有威脅的後方來車;當車輛由盲點區域接近時，系統會發出警示提醒駕駛注意，以達到避免後方來車追撞的問題。
我們 的系統分為七個部份，各別是定義偵測區域、偵測與篩選特徵點、估算光流向量、光流向量篩選、光流向量群聚、車底陰影偵測、及等速車輛偵測。為了效率因素的考量，先定義出一個偵測區域，並且在偵測區域篩選出特徵點後，估算光流向量，然後再將光流向量依照各光流所在位置的歐幾里得距離及光流長度群聚成移動中的物件。當偵測有移動物件靠近時，再並且利用車底陰影確認車輛後，即發出警示。當來車與主體車等速時，光流向量會趨近於零，因此無法透過動態資訊偵測到來車，這時我們再利用靜態資訊來判斷是否有等速車輛存在;若有等速車輛，系統也即發出警示提醒駕駛。
我們所提出的側邊盲點偵測演算法可適應多種天氣變異與行車狀況，在各種情況下平均偵測率 (accuracy) 為 96%，在 TI (Texas Instrument) DaVinci DM6437下執行效能可達每秒10張畫面，而在個人電腦下則可達每秒 30 到 35 張畫面。

In these decades, traffic accidents have increased year after year. For traffic safety, many companies and research institutes developed real-time vehicle safety systems. When driving on the road, the view fields beside the host vehicle are limited and the drivers can not clearly observe both sides of the blind spot area; then accidents may occur when the driver changes lane. In order to avoid the accidents, we use a camera mounted under a side-view mirror and detect the vehicles with computer-vision technology.
The proposed blind-spot detection system consists of seven stages: defining the detection zone, feature point detection and filtering, optical flow estimation, optical flow filtering, optical flow grouping, vehicle underneath shadow detection, and still object detection. The dynamic information can detect moving objects, but can not detect vehicles which are still relative to the host vehicle. Thus, we include static information to assist detecting these objects.
The proposed detection system has been test in various weather conditions including sunny, cloudy, rainy day, night, etc. In experiments, the detection rate is about 97% in sunny day, 95% in cloudy day and 92% in night. The performance of the proposed system reaches 30 frames per second on an Intel? Core 2 Duo? E8300 2.83 GHz CPU, 2GB DDR RAM running on Microsoft? Windows 7.

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