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Object tracking is an area of study of great interest to various researchers, where the main objective is to improve estimation of the trajectory of a moving object. This is due to the fact that in the object tracking process there are usually variations between the true position of the moving object and the estimated position, that is, the object is not exactly followed throughout its trajectory. These variations can be thought of as Colored Measurement Noise (CMN) caused by the object and the movement of the camera frame. In this paper, we treat such differences as Gauss-Markov colored measurement noise.We use Finite Impulse Response and Kalman Filters with a recursive strategy on the tracking: predict and update. To demonstrate the filter with the best performance, tests were carried out with simulated trajectories and with benchmarks from a database available online. The UFIR modified for CMN algorithm showed favorable results with high precision and accuracy in the object tracking process with benchamark data and under no ideal conditions.While KF CMN showed better results in tests with simulated data under ideal conditions.

Visual object tracking is commonly accompanied with large variations in the image frame size and position, as caused by the object dynamics and low frame rate. In this paper, we treat such variations as a Gauss-Markov colored measurement noise (CMN), modify the Kalman filter and unbiased finite impulse response filter using the backward Euler method and employing measurement differencing, and apply to some simulated and benchmark data. It is shown that the modified filters can suppress efficiently both the slow frame variations associated with CMN and fast variations associated with white noise. Extensive experimental investigations conducted for the "Car4" benchmark database has demonstrated a high efficiency of the modified algorithms.