Abstract:  An algorithm of moving object detection fusing nonnegative matrix factorization (NMF) and vector similarity analysis is proposed. Firstly, the background is reconstructed from the continuous image sequence by using the modified NMF algorithm. Then, the similarity between the detected pixel and the recovered background model is analyzed, and the background and foreground are distinguished according to the similarity. In order to reduce the amount of computation and reduce the interference of dynamic background to the detection results, the method of kernel density estimation (KDE) is used to estimate the motion area before the similarity analysis is performed. The experimental results show that the proposed algorithm can recover the background image more accurately and detect the moving object effectively.

Key words: image processing, moving object detection, NMF, kernel density estimation, region extraction