Scalable Motion Analysis Based Surveillance Video De-Noising

In this paper, a low-complexity yet powerful method based on scalable motion analysis (SMA) is proposed. SMA mainly aims at de-noising, especially for surveillance videos in bad illumination. When we do research on surveillance videos, finding that stationary objects' noise can be removed by averaging adjacent frames, but moving objects' in videos will be fuzzy. So video de-noising can be transferred to problem of tracking and filtering along their trajectories. Three main steps exist in SMA, initial motion estimation, motion refinement and smooth, filtering along motion trajectory. Novel `Y' detector is designed in the first step to accelerate searching procedure, where temporal and spatial vectors are considered. Besides, a jitter table is used to model singular motions. All possible vector candidates are minimized to a small but reliable set. In the second step, motion reliability is considered to deal with discontinuity of motion vector filed (MVF). Finally, a filter is adopted to eliminate irrelevant vectors in motion trajectory. The proposed filter can smooth MVs in an optimal way with maximum smoothness. Comparing with popular algorithms, SMA constraints searching region and prevent many complex transformations to a great extent. Moreover, it can be easily FPGA implemented. Numbers of experiments demonstrate that SMA is robust with good performance.