Moving Object Tracking using Gaussian Mixture Model and Optical Flow

In this paper, we propose a new tracking method that uses Gaussian Mixture Model (GMM) and Optical Flow approach for object tracking. The GMM approach consists of three different Gaussian distributions, the average, standard deviation and weight respectively. There are two important steps to establish the background for model, and background updates which separate the foreground and background. This paper combines the GMM and Optical Flow object tracking. The advantages of Optical Flow are quick calculations and the disadvantage is a lack of complete object tracking. The advantage of GMM is complete results of the operation the disadvantage is not a complete object tracking, GMM result of the operation complete but disadvantages include computing for a long time with more noise. These two methods can complement each other and image filtering results in the successful tracking of objects. It has variety of uses such as video communication and compression, traffic control, medical imaging and video editing. Keywords— Object Tracking, Gaussian Mixture Model, Optical Flow, Background Subtraction, Moving Objects