Abstract
Background subtraction is a facile way to localize the moving object in video sequences which provide sufficient different intensities of foreground pixels from the background. It has been observed that the motion detection task can be more challenging in some complex scenes, which exhibit sudden or gradual illumination, varying speed of object, similarly colored background, and shadows. In that concern, we propose an efficient motion detection method based on the initialization of background, which is further used for the foreground detection. Under this formulation, we have adopted the local property of the initial motion field to get the suitable threshold condition to make it generic for the adequate visual representation of foreground pixel intensity. The effectiveness of this method can be seen when it is compared qualitatively and quantitatively to other well-known background subtraction methods. The experimental results show that it can work well in static and dynamic backgrounds condition.
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References
Vosters, L., Shan, C., Gritti, T.: Real-time robust background subtraction under rapidly changing illumination conditions. Image Vis. Comput. 30, 1004–1015 (2012)
Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE Trans. Image Process. 14, 294–307(2005)
Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22, 747–757(2000)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. IJCAI 81, 674–679 (1981)
Piccardi, M.: Background subtraction techniques: a review. IEEE International Conference, vol. 4, pp. 3099–3104 (2004)
Manzanera, A., Richefeu, J.C.: A robust and computationally efficient motion detection algorithm based on Σ-Δ background estimation. ICVGIP, 46–51 (2007)
Huang, S.-C.: An advanced motion detection algorithm with video quality analysis for video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 21, 1–14 (2011)
Oral, M., Deniz, U.: Center of mass model: a novel approach to background modeling for segmentation of moving objects. Image Vis. Comput. 25, 1365–1376 (2007)
Manzanera, A., Richefeu, J.C.: A new motion detection algorithm based on Σ-Δ background estimation. Pattern Recogn. Lett. 28, 320–328 (2006)
Huang, S.-S., Fu, L.-C., Hsiao, P.-Y.: Region-level motion-based background modeling and subtraction using MRFs. IEEE Trans. Image Process. 16, 1446–1456 (2005)
Do, B.-H., Huang, S.-C. : Dynamic background modeling based on radial basis function neural networks for moving object detection. IEEE International Conference on Multimedia and Expo (ICME 2011), pp. 1–4 (2011)
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Kumar, S., Yadav, J.S. (2016). An Efficient Motion Detection Method Based on Estimation of Initial Motion Field Using Local Variance. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 381. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2526-3_43
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DOI: https://doi.org/10.1007/978-81-322-2526-3_43
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