本研究提出一套偵測貨櫃車車尾區域影像之方法，擷取出的貨櫃車車尾影像，可用來當作貨櫃的外觀特徵 (appearance feature)，以強化貨櫃的識別監視系統。本方法首先以匹配濾波器偵測不同角度的線段，之後找出這些線段所組合而成的所有四邊形，再針對這些四邊形擷取特徵進行過濾，找出符合貨櫃車車尾樣式的四邊形，在我們實驗的過程中，辨識率高達99.8%，證明本方法在貨櫃車車尾區域的偵測問題上，確實可行。

This paper proposed a method for detecting the container rear end images. The container rear end images can be regarded as appearance features of a container to enhance the container recognition and surveillance system. To detect the container rear end images, the method uses a matched filter to detect different directions of lines in the container rear end images. Then, we find quadrilaterals which are composed of all combinations of lines. Afterwards, quadrilateral features are extracted and fed into a support vector machine (SVM) classifier to train a model for container rear end image recognition. Then, the model is used to detect the quadrilaterals from container rear end images in the testing phase. Finally, a correct procedure is used to correct the classification error. In the experimental results, the accuracy rate is 99.8%. The result shows that the proposed method can be applied to the container rear end images recognition.

書名頁 ..................................ii
審定書 ..................................iii
中文摘要 ..................................iv
英文摘要 ..................................v
誌 謝 ...................................vi
目 錄 ...................................vii
第一章、 序論 ............................1
1.1 研究背景 .............................1
1.2 本研究之貢獻 .............................2
1.3 論文架構 .............................2
第二章、 文獻探討 ......................3
2.1 物件偵測 .............................3
2.2 車輛偵測 .............................5
2.3 線段偵測 .............................6
第三章、 研究方法 .....................8
3.1貨櫃車影像結構介紹 ....................8
3.2貨櫃偵測流程 ...........................9
3.3貨櫃影像前處理 ...........................10
3.4匹配濾波器 ...........................13
3.4.1匹配濾波器核心設置 ..................14
3.4.2線段合併 ...........................18
3.4.3線段交點偵測 ...........................22
3.5 特徵擷取 ............................23
3.6 支援向量機 ..........................29
3.7 分類器錯誤校正 ......................32
第四章、 實驗結果與討論 ................36
4.1 資料來源 ............................36
4.2 實驗流程 ............................37
4.3 貨櫃車尾區域偵測評估方式 .............38
4.4 貨櫃車尾區域偵測之測試結果 ...........39
第五章、 結論與未來展望 ...............41
參考文獻 ................................42

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