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Fuzzy System-based Real-Time Face Tracking With Pan-Tilt-Zoom Camera
|關鍵字:||Face Tracking;人臉追蹤||出版社:||電機工程學系所||引用:|| E. Hjelmas and B.K. Low, “Face Detection: A Survey,” Computer Vision and Images Understanding, vol. 83, pp. 236-274, 2001.  M.Yang, D. Kriegman, and N. Ahuja, “Detecting Faces in Images: A Survey,” Proc. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan. 2002.  C. Kotropoulos and I. Pitas, “Rule-based face detection in frontal views,” Proc.IEEE Int'l Conf. Acoustics, Speech and Signal Processing, vol. 4, pp. 2537-2540, 1997.  Y. B. Sun and J. T. Kim, and W. H. lee, “Extraction of face objects using skin color information,” In Proc. IEEE Int. Conf. Communications, Circuits and Systems and West Sino Expositions, vol. 2, pp. 1136-1140, 2002.  K.C. Yow and R. Cipolla, “Feature-Based Human Face Detection,” Proc. IEEE Cnf. Image and Vision Computing, vol. 15, no. 9, pp. 713-735, 1997.  R. L. Hsu, M. A. Mottaleb, and A. K. Jain, “Face detection in color images,” IEEE Trans. IEEE Trans. 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SICE-ICASE (Society of Instrumentation and Control Engineers - Institute of Control,Automation and Systems Engineers), pp. 2985-2988, Oct. 2006  C. Lerdsudwichai, and M. Abdel-Mottaleb, “Algorithm for multiple faces tracking,” Proc. Int. Conf. Pattern Recognition, Vol. 3, pp.977-980, Aug. 2004.  CIT face database http://www.vision.caltech.edu/Image_Datasets/faces/||摘要:||
This thesis proposes a real-time face tracking system with a pan-tilt-zoom camera using the technique of fuzzy systems. Tracking is based on detected faces in the HSV (Hue-Saturation-Value) color space. To detect faces, a Self-Organizing TS-type Fuzzy Network with Support Vector learning (SOTFN-SV) is proposed to segment skin colors in the HS color space. To reduce the influence of illumination, a fuzzy system is designed to adaptively determine the SOTFN-SV segmentation threshold according the V color space in each image. Detected skin regions are considered as face candidates. A shape filter followed by a SOTFN-SV based facial color filter is employed to the detected face candidates to exclude components from the face candidates and reduce the number of false alarms. For face tracking, a Kalman filter algorithm is employed for face trajectory correction and prediction. The prediction function helps to narrow down the face detection region so that the detection time can be reduced. To track a specific person when there are multiple persons appearing in the detection region, the dressing color information of each detected person is used. In the circumstance that the tracked person is back to the camera and no face is detected, the consecutive frame difference is employed to track the moving person to avoid tracking lost. Performance of the proposed face detection method is compared to other real-time face detection methods and the results show that the proposed method achieves a better detection result. In real-time operation, the panning and tilting operation in camera is employed to keep the moving person located in the captured image, and the zooming operation is employed to keep a suitable detection face in the image. Experiments on different environments show good real-time face tracking results.
|Appears in Collections:||電機工程學系所|
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