Motion Vector Estimation Method of Dynamic Image Sequence Using Neural Network in the Context of Internet of Things

Abstract

Abstract To improve the low resolution of dynamic images caused by motion vector, a neural network-based motion vector estimation method for dynamic image sequences is proposed in this study. First, a sum of absolute differences (SAD) method is used to determine the search range of motion vector estimation for dynamic image sequences, and a sped up robust features (SURF) algorithm is used to extract the motion vector features from the determined search range. Then, the self-organizing neural network is used to obtain the motion vector estimation results according to the motion vector features of the extracted dynamic images. Finally, a weighted median vector filter is used to correct the pseudo-nonlinear motion vector in the motion vector estimation results to improve the motion vector estimation performance of dynamic image sequences. The experimental results show that the algorithm can compensate for the image according to the motion vector estimation results of dynamic image sequences, and the image quality is obviously improved with high peak signal-to-noise ratio.