Detection and Moving Object Tracking in images using an improved Kallman Filter (KF) by an Invasive weed optimization algorithm

Nowadays, Moving Object Tracking in real time is a new paradigm that has a great impact on video surveillance to identify and track objects, and due to the constant change in the motion of the object and changing the size of the scene, obstruction, changes in appearance and changes in movement and brightness of one It is one of the important research fields and a very important task in the field of automated security automation monitoring systems. On the other hand, object tracking in a film is a matter of estimating positions and other related information about the movement of objects in the film’s visual sequences. The first step in such systems is to detect a moving object in the film. The second step is to track the detected object. In this paper, Moving Object Tracking is performed using the improved Kalman Filter (KF) method. The algorithm is successfully applied to standard video datasets. The Kalman Filter detects an object by assuming the initial state and estimating the sound covariance, and provides an efficient method for calculating the state estimation process by improving its initial parameters using the Invasive weed optimization (IWO) algorithm. Experimental results will be compared on MATLAB software and the proposed algorithm will be compared with the basic article algorithm in terms of performance and accuracy.