Digital Image Processing Techniques for Object Tracking System Using Kalman Filter

The focus of this paper is to design an algorithm to track an object, moving with an unknown trajectory, within the camera?s ?eld of view.To achieve this the Kalman Filter (KF) is used for tracking and estimation because of its simplicity, optimality, tractability and robustness.It is also known as linear quadratic estimation (LQE). The Kalman ?lter, de?ned by simulation was applied to a DVT sensor to determine the actual performance. The Single Filter method was implemented. The Single Filter was able to track high speed an error of a couple pixels. By using this algorithm a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone. It will helpful to track an object, moving with an unknown trajectory, within the camera?s ?eld of view (FOV). The Kalman ?lter uses the measured position of the target?s centroid as well as previous state estimates to determine the position of the centroid in the next time step. More formally, the Kalman filter operates recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state.