Efficient Object Tracking Using Optimized K-means Segmentation and Radial Basis Function Neural Networks Alireza Asvadi Faculty of Electrical and Computer Engineering Babol University of Technology Babol, Iran asvadi.a@stu.nit.ac.ir MohammadReza Karami Faculty of Electrical and Computer Engineering Babol University of Technology Babol, Iran mkarami@nit.ac.ir Yasser Baleghi Faculty of Electrical and Computer Engineering Babol University of Technology Babol, Iran y.baleghi@nit.ac.ir Abstract—In this paper, an improved method for object tracking is proposed using Radial Basis Function Neural Networks. Optimized k-means color segmentation is employed for detecting an object in first frame. Next the pixel- based color features (R, G, B) from object is used for representing object color and color features from surrounding background is extracted and extended to develop an extended background model. The object and extended background color features are used to train Radial Basis Function Neural Network. The trained RBFNN is employed to detect object in subsequent frames while mean-shift procedure is used to track object location. The performance of the proposed tracker is tested with many video sequences. The proposed tracker is illustrated to be able to track object and successfully resolve the problems caused by the camera movement, rotation, shape deformation and 3D transformation of the target object. The proposed tracker is suitable for real-time object tracking due to its low computational complexity. Keywords-component; computer vision; object tracking; k-means segmentation; radial basis function neural networks; mean shift. I. INTRODUCTION Tracking is basic task for several applications of computer vision, e.g., video monitoring systems, video indexing, traffic monitoring, automated surveillance, and so on. The aim of an object tracker is to generate the trajectory of an object over time by locating its position in every frame of the video. Object tracker may also provide the complete region in the image that is occupied by the object at every time instant. Some of important challenges encountered in visual tracking are non-rigid objects, complex object shapes, occlusion, scale/appearance change of the objects and real-time processing. Considerable work has already