Detection of Small Moving Targets in Staring Images Sequence with Complex Background and Low Contrast

A detection algorithm for small moving targets is proposed. The new algorithm firstly utilizes convolution filtering for noise smoothing, and then a proposed preprocessing method based on the norm of the difference vectors of the processed images sequence is applied to remove most of low-frequency background. Furthermore, optic flow technique is adopted to segment the doubtful small moving targets from the subimage remained by preprocessing. Finally, the statistic information for each of doubtful small moving targets is calculated. From the statistical feature, a determining criterion is established to determine whether each of the doubtful small moving targets is a true target or not. Because the preprocessing approach can get rid of most of the low-frequency background effectively, the calculation quantity of the sequential processing by optic flow is decreased largely. The experiments in a designed test system prove that the proposed detection algorithm can detect small moving targets in 30fps, 512x512 pixels, staring images sequence with SNR no less than 3dB, and the correct detecting probability is up to 96%, which can satisfy the real time processing requirements in practice.