人體動作辨識系統在電腦視覺領域一直是很熱門的研究與應用目標。在居家監控系統中最常見的方式是，使用固定式的攝影機，對室內的人物進行追蹤與動作辨識。為了達到即時監控之目標，處理的演算法必須快速，而且又必須能夠有效的分析影像。
在本論文中，動作辨識的目標是人體，為了更正確的擷取出人體部份，我們同時使用灰階域與HSV色彩空間，建立兩個背景模型，提升消除影像中陰影部分之效果，使得前後景之分離結果能夠更完整。我們以5：1降低取樣頻率，取得即時影像，擷取出的前景部份，經過特徵空間轉換與標準空間轉換後，累積三張上述降頻取樣動作影像後，藉由預先學習而建立之模糊法則與時序動作姿態比對，完成人體動作之辨識。當人在室內活動時，系統能夠依據在YCbCr色彩空間中，藉由建立之衣服色彩模型，進行人物之辨識與追蹤。此系統可以追蹤某人於何時進入或離開此房間及辨識某人在此期間的動作。

第一章 研究介紹 1
1.1 研究動機 1
1.2 前後景分離 2
1.3 特徵空間轉換與標準空間換 3
1.4 動作辨識 4
1.5 論文架構 4
第二章 基礎觀念介紹 5
2.1 特徵空間轉換與標準空間轉換介紹 5
2.1.1 特徵空間轉換(EST) 6
2.1.2 特徵空間轉換(CST) 8
2.2 HSV色彩空間介紹 10
第三章 動作辨識系統 12
3.1 建立背景模型 12
3.2 前後景分離 14
3.3 陰影濾波器 15
3.4 前景影像補償與處理 17
3.5 背景模型更新 19
3.6 選擇樣版動作 20
3.7 由影像串流建立模糊法則與動作辨識 21
第四章 實驗結果 25
4.1 背景模型建立與前景擷取 26
4.2 建立辨識動作之模糊法則 37
4.3 動作辨識正確率 42
第五章 結論 45
參考資料 46

[1] J. Yamato, J. Ohya, and K. Ishii, “Recognizing Human Action in Time-Sequential Images using Hidden Markov Model,” In Proc. IEEE CVPR, pp. 379 385, 1992.
[2] I. Haritaoglu, D. Harwood, and L. S. Davis, “W4 : Real-Time Surveillance of People and Their Activities,” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809 830, 2000.
[3] T. Horprasert, D. Harwood, and L.S. Davis, “A Statistical Approach for Real-Time Robust Background Subtraction and Shadow Detection,” in Proc. IEEE ICCV’99, 1999.
[4] R. Cucchiara, C. Grana, M. Piccardi and A. Prati, “Improving Shadow Suppression in Moving Object Detection with HSV Color Information,” in Proc. IEEE Intelligence Transportation System Conference, pp. 334 339, 2001.
[5] R. Cucchiara, M. Piccardi and A. Prati, “Detecting Moving Objects, Ghosts, and Shadows in Video Streams,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1337 1342, 2003
[6] A. Prati, I. Mike, M. Trivedi and R. Cucchiara, “Detection Moving Shadow: Algorithms and Evaluation,” in Proc. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 7, pp. 918 923, 2003.
[7] S. Vitabile, G. Pilato, G. Pollaccia, and F. Sorbello, “Road Signs Recognition Using a Dynamic Pixels Aggregation Technique in the HSV Color Space,” in Proc. 11th International Conference on Image Analysis and Processing, pp. 572 577, 2002.
[8] H. Saito, A. Watanabe, and S. Ozawa, “Face pose estimating system based on eigenspace analysis,” in Proc. Int. Conf. Image Processing, vol 1, pp. 638 642, 1999.
[9] J. Wang, G. Yuantao, K. N. Plataniotis, and A. N. Venetsanopoulos, “Select eigenfaces for face recognition with one training sample per subject,” in Proc. 8th Cont., Automat. Robot. Vision Conf., ICARCV 2004, vol. 1, pp. 391 396, 2004.
[10] M. M. Rahman and S. Ishikawa, “Robust appearance-based human action recognition,” in Proc. The 17th Int. Conf. Pattern Recog., Vol. 3, pp. 165 168, 2004.
[11] L. R. Rabiner, “A tutorial on hidden Markov model and selected applications in speech recognition,” in Proc. IEEE, vol. 77, no. 2, pp 257 286, 1989.
[12] L. Nianjun, B. C. Lovell, and P. J. Kootsookos, “Evaluation of HMM training algorithms for letter hand gesture recognition,” in Proc. the 3rd IEEE Int. Symposium Signal Processing Inform. Technol., ISSPIT 2003, pp. 648 651, 2003.
[13] K. Etemad and R. Chellappa, “Discriminant analysis for recognition of human face images,” in Proc ICASSP, pp. 2148 2151, 1997.
[14] B. Chen and Y. Lei, “Indoor and Outdoor People Detection and Shadow Suppression by Exploiting HSV Color Information,” Fourth International Conference on Computer and Information Technology, pp 137 142, 2004.
[15] K. Ohba, Y. Sato, and K. Ikeuchi, “Appearance-based visual learning and object recognition with illumination invariance,” Machine Vision and Application, vol. 12, no. 4, pp. 189 196, 2000.
[16] Soriano M, Huovinen S, Martinkauppi B, Laaksonen M. “Using the skin locus to cope with changing illumination conditions in color-based face tracking,” in IEEE Nordic Signal Processing Symposium, kolmarden, Sweden, pp. 383 6, 2000.
[17] Y. C. Luo, “Extracting the Foreground Subject in the HSV Color space and Its Application to Human Activity Recognition System,” Master Thesis, Elect. and Con. Eng. Dept., Chiao Tung Univ., Taiwan, 2007.
[18] L. X. Wang and J. M. Mendel, “Generating fuzzy rules by learning from example,” IEEE Trans. Syst., Man Cybern, vol. 22, no. 6, pp. 1414 1427, 1992.