隨著車用電子科技的進步，先進駕駛輔助系統變得越來越重要，特別是移動物偵測在先進駕駛輔助系統中扮演著重要角色。在一般市區道路上，移動物主要可以分為行人、機車、腳踏車和汽車這四個物件。在實際應用中，移動物偵測系統存在兩個嚴峻的挑戰:需要大量的計算時間和精確的偵測率。因此，為了克服這些問題，我們提出了一個新穎的移動物偵測系統，其中包含了預處理、特徵擷取、分類和狀態機。在預處理中，利用跳過低繁忙點的偵測視窗，能夠有效提升計算時間以解決上述提及之問題。另外，為了提升偵測率，我們提出了基於方向均化梯度權重直方圖法結合支持向量機。除此之外，我們還使用狀態機能以更進一步提升系統的強健性。最後，在實驗結果方面，利用我們所錄製的市區測試影片，將本論文所提出的統和原本方向均化梯度直方圖之系統進行比較，實驗證明，我們提出的系統相較於舊有系統有更好的偵測效果，也能夠達到即時偵測來以提醒駕駛者前方有移動物。

With the growth of the automotive electronics technology, the advanced driver assistance system (ADAS) becomes more and more important. Especially, the moving object detection (MOD) is an important issue in the ADAS. There are four important moving objects, pedestrian, scooter, bicycle and car in normal urban roads. In realistic systems, there exist two critical challenges including computing time and detection rate for MOD. To overcome these problems, we propose a novel moving object detection system which contains pre-processing, feature extraction, classification and state machine. The pre-processing, skipping low busyness windows, accelerates the computing time to solve the mentioned problem. Additionally, the weighted histogram of oriented uniform gradient (WHOUG) with support vector machine (SVM) is proposed to promote the detection accuracy. Besides, the finite state machine could further improve the robustness of the proposed system. In experimental results, the proposed system is compared to the original HOG system for testing self-collected urban videos. The results demonstrate that the proposed system achieves better performance and real-time detection than the traditional one.

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