Pseudo-ground Truth Trajectory From Contaminated Data of Object Tracking Using Smoothing Algorithms

Abstract

Object tracking is a study area of great interest to various researchers whose main objective is to improve the trajectory estimation for object tracking. In practical applications, the information available that allows the application of algorithms to improve the tracking process sometimes is missing. One of the main obstacles is obtaining ground truth, which takes a long processing time. There are manual methods and applications of reference algorithms. On the other hand, in most cases, the tracking information obtained using a camera is contaminated with noise during the acquisition process. In this paper, we applied smoothing algorithms to compute a pseudo-ground truth achieving lower estimation errors and higher precision than the measurement data. The test results showed that the proposed algorithms with the highest performance are q-lag UFIR and q-lag ML FIR. These smoothing algorithms can be useful in practical applications in object-tracking tasks.