One of the main goals of computer vision is to enable computers to replicate the basic functions of human vision such as motion perception and scene understanding. To achieve the goal of intelligent motion perception, much effort has been spent on object tracking, which is one of the most important challenges in computer vision topics. The formulations of mathematical models of many systems are basic steps in the process of evaluating their behavior; unfortunately, such formulations may become too complex or may not even be possible. Consequently, empirical functional relationships are often developed to describe system behavior using experimental data. Curve fitting, also known as regression analysis, is used to find the "best fit" line or curve for a series of data points. Most of the time, the curve fit will produce an equation that can be used to find points anywhere along the curve. This study proposes new methods to deal with the trajectory by converting the trajectory points into approximation function using curve fitting function to smooth the data; improving the appearance of the trajectory, extracting important features such as slope and intersection point.

Object tracking curve fitting motion trajectory spatio-temporal.