Bounding Box Stabilization for Visual Object Tracking Using Kalman and FIR Filters

In visual object tracking, the estimation of the trajectory of a moving object is a widely studied problem. In the object tracking process, there are usually variations between the real position of the objet in the scene and the estimated position, that is, the object is not exactly followed throughout its trajectory. These variations can be considered as color measurement noise (CMN) caused by the object and the camera frame movement. In this paper, we treat such differences as Gauss-Markov coloring measurement noise. We use Finite Impulse Response filters and Kalman filter with a recursive strategy in tracking: predict and update. To demonstrate the best performance, tests were carried out with simulated trajectories and with benchmarks from a database available online. The OUFIR and UFIR algorithms showed favorable results with high precision and accuracy in the object tracking task.