A sub-region image registration algorithm with weight-based hierarchical importance sample consensus for moving target detection

The image registration is the key part in moving target detection (MTD), including extracting feature points, matching feature points and estimating motion model. While MTD always confronts with camera irregular movement and complex scenes where many small moving targets are mixed with the background, the accuracy of motion model estimation is seriously affected by these problems. So, our job focuses on estimating a motion model. We present a novel sub-region image registration algorithm with weight-based hierarchical importance sample consensus (WHISAC) to efficiently estimate motion model. Image registration based on sub-region is for removing background motions in different regions called sub-region, and the motion models of sub-regions are separately estimated. Then, in WHISAC, for overcoming the flaws that the ratios of outliers' increases in sub-regions and small moving targets are regarded as inliers wrongly, weights combining into a quality function are proposed to indicate the types of correspondences for guiding sampling. Samples in the WHISAC are drawn hierarchically a different number in the data set sorted by the quality function. Finally, the motion model based on the consensus is obtained by WHISAC. Compared to RANSAC and LMeds, WHISAC has more accurate consensus, faster convergent speed and different images with less interference which facilitates the subsequent target detections in the MTD system.