A robust motion detection algorithm on noisy videos

The applicability and performance of motion detection methods dramatically degrade with the increasing noise. In this paper, we propose a robust dictionary-based background subtraction approach, which formulates background modeling as a linear and sparse combination of atoms in a pre-learned dictionary. Motion detection is then implemented to compare the difference between sparse representations of the current frame and the background model. The projection of noise over the dictionary being irregular and random guarantees the adaptability of our approach. Experimental results on synthetic and real noisy videos demonstrate the robustness of the proposed approach compared to other methods.