Abstract
The first step in various computer vision applications is a detection of moving objects. The prevalent pixel-wise models regard image pixels as independent random processes. They don’t take into account the existing correlation between the neighboring pixels. By using a nonparametric density estimation method over a joint domain-range representation of image pixels, this correlation can be exploited to achieve high levels of detection accuracy in the presence of dynamic backgrounds. This work improves recently proposed joint domain-range model for the background subtraction, which assumes the constant kernel bandwidth. The improvement is obtained by adapting the kernel bandwidth according to the local image structure. This approach provides the suppression of structural artifacts present in detection results when the kernel density estimation with constant bandwidth is used. Consequently, a more accurate detection of moving objects can be achieved.
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References
Wren, C., Azarbayejani, A., Darrel, T., Pentland, A.: Pfinder: Real Time Tracking of the Human Body. IEEE Trans. Pattern Analysis and Machine Intelligence (1997)
Stauffer, C., Grimson, W.: Learning Patterns of Activity Using Real-Time Tracking. IEEE Trans. Pattern Analysis and Machine Intelligence (2000)
Elgammal, A., Harwood, D., Davis, L.: Background and Foreground Modeling Using Non-Parametric Kernel Density Estimation for Visual Surveillance. In: Proc. IEEE, IEEE, Los Alamitos (2002)
Isard, M., Blake, A.: Condensation—Conditional Density Propagation for Visual Tracking. Proc. Int’l J. Computer Vision 29(1), 5–28 (1998)
Comaniciu, D., Ramesh, V., Meer, P.: Real-Time Tracking of Non-Rigid Objects Using Mean Shift. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition, IEEE, Los Alamitos (2000)
Haritaoglu, I., Harwood, D., Davis, L.: W4: Real-Time of People and Their Activities. IEEE Trans. Pattern Analysis and Machine Intelligence (2000)
Jain, R., Nagel, H.: On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes. IEEE Trans. Pattern Analysis and Machine Intelligence (1979)
Friedman, N., Russell, S.: Image Segmentation in Video Sequences: A Probabilistic Approach. In: Proc. 13th Conf. Uncertainity in Artificial Intelligence (1997)
Ren, Y., Chua, C.-S., Ho, Y.-K.: Motion Detection with Nonstationary Background. Machine Vision and Application (2003)
Oliver, N., Rosario, B., Pentland, A.: A Bayesian Computer Vision System for Modeling Human Interactions. IEEE Trans. Pattern Analysis and Machine Intelligence (2000)
Monnet, A., Mittal, A., Paragios, N., Ramesh, V.: Background Modeling and Subtraction of Dynamic Scenes. In: IEEE Proc. Int’l Conf. Computer Vision, IEEE, Los Alamitos (2003)
Zhong, J., Sclaroff, S.: Segmenting Foreground Objects from a Dynamic Textured Background Via a Robust Kalman Filter. In: IEEE Proc. Int’l Conf. Computer Vision, IEEE, Los Alamitos (2003)
Sheikh, Y., Shah, M.: Bayesian Modeling of Dynamic Scenes for Object Detection. IEEE Trans. Pattern Analysis And Machine Intelligence 27(11) (2005)
Seki, M., Wada, T., Fujiwara, H., Sumi, K.: Background detection based on the cooccurrence of image variations. In: Proc. of CVPR 2003, vol. 2, pp. 65–72 (2003)
Parzen, E.: On Estimation of a Probability Density and Mode. Annals of Math. Statistics (1962)
Rosenblatt, M.: Remarks on Some Nonparametric Estimates of a Density Functions. Annals of Math. Statistics (1956)
Wand, M., Jones, M.: Kernel Smoothing. Monographs on Statistics and Applied Probability (1995)
Mittal, A., Paragios, N.: Motion-based Background Subtraction Using Adaptive Kernel Density Estimation. In: IEEE Conference in Computer Vision and Pattern Recognition (CVPR), IEEE, Los Alamitos (2004)
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Antić, B., Crnojević, V. (2007). Joint Domain-Range Modeling of Dynamic Scenes with Adaptive Kernel Bandwidth. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_70
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DOI: https://doi.org/10.1007/978-3-540-74607-2_70
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