Research on Shaking Video Image Stabilization Algorithm for Moving Object Detection

Video captured by surveillance equipment will jitter due to the shaking of the equipment, this jitter will affect the detection results of moving target detection algorithms that rely on stable video frames. This paper proposes an improved ORB algorithm to solve the video jitter problem, so that the moving target detection algorithm can accurately detect moving targets in jittery videos. First, wavelet transform is used to locate the high-frequency area of the image, and then feature extraction is performed on the area, which improves the efficiency of ORB feature extraction. And use Boosted Efficient Binary Local Image Descriptor (BEBLID) to replace the descriptors of directional FAST and rotation BRIEF (ORB) to improve matching accuracy. In the feature matching stage, a neighborhood query method is proposed to replace the global search of the traditional BFMatcher, which improves the matching speed. Finally, Progressive Sample Consistency (PROSAC) is employed to ensure accurate matching of point pairs, resulting in a motion matrix for video stabilization. Finally, a Gaussian mixture model with adaptive distribution numbers is combined to quickly detect moving targets. Comparative experiments with scale-invariant feature transform(SIFT), Accelerated-KAZE(AKAZE), ORB, Qtree _ ORB and SIRB prove the superior accuracy and speed of this algorithm.