Object Tracking is becoming increasingly important in areas of computer vision, surveillance, image processing and artificial intelligence. The advent of high powered computers and the increasing need of video analysis has generated a great deal of interest in object tracking algorithms and its applications. This said it becomes even more important to evaluate these algorithms to quantify their performance. In this paper, we have implemented three algorithms namely Alpha Beta filter, Kalman filter and Meanshift to track an object in a video sequence and compared their tracking performance based on various parameters in normal and noisy conditions. The proposed parameters employed are error plots in position and velocity of the object, Root mean square error, object tracking error, tracking rate and time taken to track the object. The goal is to illustrate practically the performance of each algorithm under such conditions quantitatively and identify the algorithm that performs the best.