A Novel Approach for Classification of Moving Object with GCM and PCA-GCM Method

Resumen

In this paper, we propose a new tracking method that uses Gaussian combination Model (GCM) and PCA-GCM 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.