Object tracking with movement prediction algorithms

The task of tracking an object becomes tedious when the object moves through a dynamic background and the camera also has a random motion. This type of problem has three main aspects - object detection, prediction of object motion and compensation of the camera motion. In this paper, we have developed three algorithms using three different object detection algorithms, namely background subtraction, template matching and Speeded Up Robust Features (SURF). Unscented Kalman Filter (UKF) algorithm has been devised for the motion prediction of the moving object as well as to compensate for the camera movement. The proposed algorithms have been validated through extensive simulations performed on several video datasets and an analytical study has also been presented. Through the simulation results, performance of the proposed algorithms are compared.