Efficient object tracking using K means and Radial Basis Function

In the present article, an efficient method for object tracking is proposed using Radial Basis Function Neural Networks and K-means. This proposed method starts with K-means algorithm to do the segmentation of the object and background in the frame. The Pixel-based color features are used for identifying and tracking the object. The remaining background is also considered. These classified features of object and extended background are used to train the Radial Basis Function Neural Network. The trained network will track the object in next subsequent frames. This method is tested for the video sequences and is suitable for real-time tracking due to its low complexity. The objective of this experiment is to minimize the computational cost of the tracking method with required accuracy.