Moving object detection and trajectory prediction based on image processing

Abstract

For moving object detection and trajectory prediction in video images, it is necessary to perform image processing, feature extraction, and localization of the object. Therefore, this paper designs an optimized Kalman-Elman (KE) algorithm for trajectory prediction. In order to remove the noise points on the measured values in the Kalman filter algorithm and to solve the problem of the random setting of the initial weights and thresholds of the Elman neural network, we encode the above parameters and improve the two algorithms by using Particle Swarm Optimization (PSO). Quantitative values of the object feature extraction are used as input parameters of the Elman neural network. After a large amount of training, we obtain the predicted position of the moving object finally. The experimental results show that the prediction error of this method is significantly smaller when it is compared with previous methods.