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http://dx.doi.org/10.3837/tiis.2014.06.008

A Noisy Videos Background Subtraction Algorithm Based on Dictionary Learning  

Xiao, Huaxin (College of Information System and Management, National University of Defense Technology)
Liu, Yu (College of Information System and Management, National University of Defense Technology)
Tan, Shuren (College of Information System and Management, National University of Defense Technology)
Duan, Jiang (School of Economic Information Engineering, Southwestern University of Finance and Economics)
Zhang, Maojun (College of Information System and Management, National University of Defense Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.8, no.6, 2014 , pp. 1946-1963 More about this Journal
Abstract
Most background subtraction methods focus on dynamic and complex scenes without considering robustness against noise. This paper proposes a background subtraction algorithm based on dictionary learning and sparse coding for handling low light conditions. The proposed method formulates background modeling as the linear and sparse combination of atoms in the dictionary. The background subtraction is considered as the difference between sparse representations of the current frame and the background model. Assuming that the projection of the noise over the dictionary is irregular and random guarantees the adaptability of the approach in large noisy scenes. Experimental results divided in simulated large noise and realistic low light conditions show the promising robustness of the proposed approach compared with other competing methods.
Keywords
Dictionary learning; background subtraction; sparse representation; low light; noisy videos;
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