Implementation of Moving Object Detection Using Sensors
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The aspect of study is to check the level of acceptance of the system by the user. This contains the process of training the one of the user to use the system well organized. The user must not feel threatened by the system, instead must accept it as a fact of being required. The level of acceptance by the users merely depends on the methods that are employed to educate the user about the system and to make him well known with it. The Author’s level of confidence must be raised so that he is also able to make some constructive criticism, which is welcomed, as he is the final user of the system. Moving Object detection is the process of detecting a change in the position of an object with respect to its surroundings or the change in the surroundings in relation with an object. Motion detection can be achieved by both mechanical and electronic methods. When motion detection is accomplished by natural organisms, it is called motion perception
C. Stauffer and W.E.L. Grimson, “Learning Patterns of Activity Using Real-Time Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, Aug. 2000.
B. Han, D. Comaniciu, and L. Davis, “Sequential Kernel Density Approximation through Mode Propagation: Applications to Background Modelling,” Proc. Asian Conf. Computer Vision, 2004.
D.S. Lee, “Effective Gaussian Mixture Learning for Video Background Subtraction,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 827-832, May 2005.
Z. Zivkovic and F. van der Heijden, “Efficient Adaptive Density Estimation Per Image Pixel for Task of Background Subtraction,” Pattern Recognition Letters, vol. 27, no. 7, pp. 773-780, 2006.
P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
B. Han, D. Comaniciu, Y. Zhu, and L.S. Davis, “Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1186-1197,July 2008.
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