應用人體動作辨識系統於吃藥辨識及日常生活活動

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。此外，當某人要進行吃藥動作時，我們使用在HSV空間中建立好的藥包顏色色彩模型（僅考慮色調）去辨識藥包的顏色。因此，藉由結合藥包顏色色彩模型和人體動作辨識系統，我們就可以得知某人正在吃藥以及他的藥包顏色。最後，我們利用人體動作辨識系統去記錄學生的日常生活。

關鍵字

動作辨識

並列摘要

Human activity recognition system is now a very popular subject for research and application. Using a fixed camera to track a person and recognize his (her) activity is widely seen in home surveillance. For real-time surveillance, the embedded algorithms must be efficient and fast to meet the real-time constraint. In the thesis, a new person tracking and continuous activity recognition is proposed. We build two background models, in grayscale and HSV color space as well to extract the human correctly, and we could also reduce the shadowing effect well. For better efficiency and separability, the binary image is firstly transformed to a new space by eigenspace and then canonical space transformation, and the recognition is finally done in canonical space. A three image frame sequence, 5:1 down sampling from the video, is converted to a posture sequence by template matching. The posture sequence is classified to an action by fuzzy rules inference. Fuzzy rule approach can not only combine temporal sequence information for recognition but also be tolerant to variation of action done by different people and time. Moreover, we make use of the hue component to recognize the medical pouch’s color when one is taking medicine. By combining with the hue-based pouch’s color model and human activity recognition system, we can know someone is taking medicine and its medical pouch’s color as well. Finally, we also employ the activity recognition system to record a student’s activity in the daily living.

並列關鍵字

activity recognition

參考文獻

[1] F. Bobick and J. W. Davis, “The recognition of human movement using temporal templates,” IEEE Trans. Pattern Anal. Machine Intell., vol. 23, no. 3, pp. 257–267, Mar. 2001.

[3] I. Cohen and H. Li, “Inference of human postures by classification of 3D human body shape,” in Proc. IEEE Int. Workshop on Anal. Modeling of Faces and Gestures, pp. 74–81, Oct. 2003.

[4] I. Haritaoglu, D. Harwood, and L. S. Davis, “W : Real-time surveillance of people and their activities,” IEEE Trans. Pattern Anal. Machine Intell., vol. 22, no. 8, pp. 809–830, Aug. 2000.

[5] Robert H. Luke and James M.Keller, “Modelng human activity from voxel person using fuzzy logic,” IEEE Transactions on fuzzy systems, vol. 17, no.1, pp. 39–49, Feb. 2009.

[6] M. Piccardi, “Background subtraction techniques: a review,” in Proc. IEEE Int. Conf. SMC., vol. 4, pp. 3099–3104, Oct. 2004.

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應用人體動作辨識系統於吃藥辨識及日常生活活動

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