停車格狀態偵測是停車場監控管理系統的重要技術之一。在本篇論文中我們提出一個利用單個監視器獲取的單張影像來評估目前停車格狀況的演算法。通常一般最簡單的辨識方法為在每個停車格上面架設個別的感測器，但再加上後續的維護，這將會是一筆不小的費用。因為目前一般的停車場內都已經有架設監視器，我們可利用這些監視器拿來作監控系統利用，因此可另外省下購買與架設的費用。
本篇論文以電腦視覺為基礎，利用影像處理的技術發展出一種新穎的停車格狀態偵測演算法。演算法內容為結合停車格的特徵萃取與k-means分群法來判別目前停車格中的停車狀態。
最後我們將所提出的演算法實際在一個戶外停車場作驗證，總共測試了五天影像中涵蓋各種時間及天氣，如早中晚、陰天、晴天及起霧等各種天氣狀
態。測試結果證實了我們所提出演算法的精確性，正確率高達98%。

Parking cell detection is one of the key technologies in parking lot monitoring and management system. In this thesis, we propose an algorithm to estimate the occupancy of a parking cell using a single image captured by a camera. Usually, car-parks are already equipped with CCTV-cameras for surveillance purpose which can be served for automatic detection systems as well. Our system is targeted on whether a parking cell is occupied or not. Exact solutions like individual sensors are too costly.
In this thesis, we propose a vision-based system that use computer vision techniques for detecting the occupation status by using the static image captured by a single camera. Our algorithm uses the combination of parking cell features extraction and k-means clustering to discriminate occupancy status of parking spaces.
The proposed method was tested on an actual outdoor parking lot for a period of 5 days with different weather conditions from sunrise to sunset. The results confirm the feasibility of the proposed method with the accuracy rate being over 98%.

[1] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 2nd edition,
Prentice-Hall, Upper Saddle River, NJ, 2002.
[2] I. E. Sobel, “Camera Models and Machine Perception ”, Ph.D. thesis, Stanford University, pp. 99, 1970.
[3] J. Canny, “A computational approach to edge detection ”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698, 1986.
[4] J. Serra “Image Analysis and Mathematical Morphology ”, pp. 610 , Jovanovich B.H., Academic Press, Tokyo, London, 1982.
[5] G. Mathron, “Random Sets and Integral Geometric ”, New York : Wiley, 1975.
[6] R.M. Haralick, K. Shanmugam, and I. Dinstein, “Texture features for image classification” , IEEE Transactions on System, Vol-SMC-3, pp. 610-621, Nov. 1973.
[7] R. Baeza-Yates, and B. Ribeiro-Neto, , Modern Information Retrieval, Addison
Wesley, 1999.
[8] S. L. Chang, L. S. Chen, Y. C. Chung and S. W. Chen, “Automatic license plate recognition” , IEEE Transactions on Intelligent Transportation Systems, vol. 5, pp. 42-53, Mar. 2004.
[9] K. Yamada, M. Mizuno, “A vehicle parking detection method using image segmentation”, Electronics and Communications in Japan, vol.84, no.10, pp. 25-34, 2001.
[10] S. Funck, N. Mohler and W. Oertel, “Determining car-park occupancy from single images”, IEEE Intelligent Vehicles Symposium, pp. 325-328, June 2004.
[11] D.B.L. Bong, K.C Ting, and N. Rajaee, “Car-Park Occupancy Information
System” , Third Real-Time Technology and Applications Symposium 2006, Serdang, Selangor, December 2006.
[12] 連國珍，數位影像處理，四版，儒林圖書有限公司，民國八十五年。
[13] 鐘國亮，影像處理與電腦視覺，二版，東華書局，民國九十三年。
[14] 郭大正、韓欽銓，“停車場自動監視系統”，碩士論文，私立中華大學資訊工程學系，民國九十二年。
[15] 曾佩婷，“在特定車道行駛之大型車偵測與追蹤”，碩士論文，中央大學資訊工程學系，民國九十三年。
[16] 蔡洛緯，“以正規化色彩與邊緣資訊作車輛偵測”，碩士論文，中央大學資訊工程學系，民國九十三年。