Automatic Detection of Moving Point Targets in Staring Infrared Binocular Imaging System

摘要

According to the feature of remote sensing staring binocular imaging system, which is the information quantity passed through the overlapped Field Of View (FOV) is larger than that passed through the non-overlapped FOV, a new parallel high-speed automatic detecting algorithm of moving point targets is proposed. In the proposed detecting algorithm, the Difference Vector Norm of the detected images sequence is used as a preprocessing method to get rid of low-frequency noise and background pixels, then the Optical Flow Algorithm is applied to segment the doubtful moving point targets from the subimage remained by preprocessing. If doubtful moving point targets are detected by Optical Flow, the binocular system will be rotated to make the overlapped FOV direct to each of the doubtful moving point targets, and a new proposed space-time parallelizing determining approach is used to determine whether they are true moving point targets or not. Because the preprocessing can get rid of most of the low-frequency noise and background pixels, the calculating quantity of the sequential Optical Flow is reduced largely. At the same time, the new proposed determining algorithm is space-time parallel processing, which can decrease the determining time largely. The experiment results prove that the average detecting time of moving point targets of the proposed algorithm in the staring infrared binocular imaging system is reduced 50% than that of the traditional detecting approach, and if the SNR of processed images is no less than 3dB, the correct determining probability is 97%.