A method of low-rank decomposition with multi-scale product for moving object detection

Considering the small amplitude changes of the natural scene and camera shake problems that affect moving object detection based on rank decomposition, an effective algorithm of low-rank decomposition with multi-scale product is proposed. The basic theoretical proposition is that in the static background video sequences, every frame background images can be regarded approximately under the same low-rank subspace and the foreground change can be seen as sparse residuals. Firstly, the observation data matrix can be obtained through the preprocessing including filtering and affine transformation for every frame images; then, two components of the low-rank matrix and a sparse matrix can be obtained by low-rank decomposition on the image sequence; finally, with the multi-scale product the moving object edge is extracted via wavelet transform modulus maxima on the sparse foreground images, and a postprocessing by morphological is carried out to obtain the accurate moving object. Experimental results show that, by the proposed method, clear and complete objects can be obtained and this method can effectively handle the moving object detection problems under some complex situations such as light changes, the small amplitude changes of the image background and camera shake.