Detection and Tracking of Moving Object-Using Kalman Filter Enhancement (KF) by Grasshopper Optimization Algorithm

Today, the process of tracking moving objects in Video sequence has many real applications such as robots' systems, surveillance systems, and monitoring systems, visual information processing, and so more. The first stage in tracking moving object systems is detection the target in Video sequence and images and the second stage is to track the identified object. In this regard, Automatic detection and tracking of object is an attractive scope in now researches, so the proposed method based on the detection-tracking target moving objects by using the Kalman Improved Filter (KF) method. The Kalman filter, assuming initial state and noise covariance parameter for detection the object, which are critical parameters for estimating speed. For successful tracking by Kalman filter, the noise covariance matrix must be optimized. Therefore, in many studies, different methods based on optimization and metaheuristic algorithms have been proposed to increase the performance of Kalman filter method. In this research to adjust, the noise covariance is of the Kalman filter for object tracking and improve the initial parameters of it, using grasshopper optimization algorithm (GOA). Here considered not only the properties of the object, but also the estimation of the motion of the object to speed up the search process. To compare the new method's efficiency and accuracy, we used MATLAB R2019b software. The results show that the proposed method has improved at least 10% in accuracy compared to RMOT and CMOT and 20% in recovery compared to Recall. In addition, the proposed method has at least a 2% improvement in Recall parameters and accuracy compared to the evaluated article.