Abstract
Bayesian technique is a popular tool for object detection due to its high efficiency. As it compares pixel by pixel, it takes a lot of execution time. This paper addresses a novel framework for head detection with minimum time and high accuracy. To detect head from motion pictures, motion segmentation algorithm is employed. The novelty of this paper carried out with the following steps: frame differencing, preprocessing, detecting edge lines and restoration, finding the head area and cutting the head candidate. Moreover, nested K-means algorithm is adopted to find head location and statistical modeling is employed to determine face or non-face class, while Bayesian Discriminating Features (BDF) method is employed to verify the faces. Finally, the proposed system is carried out with a lot of experiments and a recognizable success is notified.
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References
Chen, M., Kee, S.: Head tracking with shape modeling and detection
Foresti, G.L.: A Real-Time System for Video Surveillance of Unattended Outdoor Environments. IEEE Transactions On Circuits And Systems For Video Technology 8(6) (1998)
Yang, T., Pan, Q., Li, J., Cheng, Y., Zhao, C.: Real-Time Head Tracking System With an Active Camera*. In: Proceedings of the 5" World Congress on Intelligent Control and Automation, Hangzhou, PR China, June 15-19 (2004)
Huang, W., Ruijing Luo, H., Lee, B.H., Rajapakse, M.: Real Time Head Tracking and Face and Eyes Detection. In: Proceedings of IEEE TENCON 2002 (2002)
Won, J.-U., Chung, Y.-S., Kim, I.-S., Choi, J.-G., Park, K.-H.: Correlation based video-dissolve detection. In: Proceedings. ITRE2003. Research and Education (2003)
Eng, H.-L., Wang, J., Kam, A.H., Yau, W.-Y.: Bayesian Framework for Robust Human Detection and Occlusion Handling using Human Shape Model. In: ICPR. 17th International Conference on Pattern Recognition (ICPR 2004), vol. 2, pp. 257–260 (2004)
Hu, W., Tan, T., Wang, L., Maybank, S.: A Survey on Visual Surveillance of Object Motion and Behaviors. IEEE Transactions On Systems, Man, And Cybernetics—Part C: Applications And Reviews 34(3) (August 2004)
Liu, C.: A Bayesian Discriminating Features Method for Face Detection. IEEE Transactions On Pattern Analysis And Machine Intelligence 25(6) (June 2003)
Haritaoglu, I., Harwood, D., Davis, L.S.: W: Real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Machine Intell. 22, 809–830 (2000)
McKenna, S., Jabri, S., Duric, Z., Rosenfeld, A., Wechsler, H.: Tracking groups of people. Comput. Vis. Image Understanding 80(1), 42–56 (2000)
Lipton, A.J., Fujiyoshi, H., Patil, R.S.: Moving target classification and tracking from real-time video. In: Proc. IEEE Workshop Applications of Computer Vision, pp. 8–14 (1998)
Meyer, D., Denzler, J., Niemann, H.: Model based extraction of articulated objects in image sequences for gait analysis. In: Proc. IEEE Int. Conf. Image Processing, pp. 78–81 (1998)
Mulassiotis, S., Strintzis, M.G.: Real-Time Head Tracking And 3d Pose Estimation From Range Data. IEEE, Los Alamitos (2003)
Nanda, H., Fujimura, K.: A Robust Elliptical Head Tracker. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition (FGR 2004) (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Joo, H.J., Jang, B.W., Bashar, M.R., Rhee, P.K. (2006). Real Time Face Detection System Based Edge Restoration and Nested K-Means at Frontal View. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_148
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DOI: https://doi.org/10.1007/11881599_148
Publisher Name: Springer, Berlin, Heidelberg
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