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Publication Type : Conference Proceedings
Source : Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer. (Best Paper Award at IIT Indore)
Url : https://link.springer.com/chapter/10.1007/978-981-16-2712-5_54
Campus : Bengaluru
School : School of Computing
Verified : No
Year : 2021
Abstract : Background subtraction being one of the most crucial steps in numerous real-world video-based applications has been studied extensively to date. Several researchers have provided solutions to this problem by exploiting the patterns and information hidden in a set of initial raw videoframes with the help of sophisticated machine learning techniques. While others have proposed to uncover the hidden patterns in raw data by employing a variety of texture patterns, which now have become an inseparable part of the background subtraction problem. Hence, the paper proposes to employ a novel feature combination of Attractive–repulsive local binary gradient contours with RGB channels and luminance information into a consensus-based modeling technique. The results obtained on sample videos from the standard Change Detection dataset [1] support for the superiority of the proposed methodology.
Cite this Research Publication : Rimjhim Padam Singh and Poonam Sharma (2021), Using Attractive Repulsive Binary Local Gradient Contours for Sample-Consensus Background Modeling. In: Tiwari A., Ahuja K., Yadav A., Bansal J.C., Deep K., Nagar A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer. (Best Paper Award at IIT Indore)