A NOVEL APPROACH FOR CLASSIFICATION OF MOVING OBJECT WITH GCM AND PCA-GCM METHOD

In this paper, we propose a new tracking method that uses Gaussian combination Model (GCM) and PCAGCM approach for traffic object tracking. The GCM approach consists of three different Gaussian distributions, the average, standard deviation and weight respectively. This paper combines the GCM and PCA-GCM for object tracking. The advantages of is to tackle tracking of moving object based on PCA-GCM together with Kalman prediction of the position and size of object along the image’s sequence. The advantage of GCM is complete results of the process the disadvantage is not a complete object tracking, GCM result of the operation complete but disadvantages include computing for a long time with high blare. The GCM and PCA-GCM can complement each other and image segmentation results in the successful tracking of objects. It has variety of uses such as compression of video and images, object rule.