Robust Object Tracking with Radial Basis Function Networks

Visual tracking has been a challenging problem in computer vision over the decades. The applications of visual tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. In this paper we present a novel object tracker based on fast learning radial basis function (RBF) networks. Here, the object and background pixel-based color features are used to develop object/non-object RBF classifiers. The posterior probability information of these classifiers are used for developing an efficient object model for tracking in the subsequent frames. The performance of the proposed tracker is tested with many video sequences of real-life complexity and compared against the color-based mean-shift tracker. The proposed tracker is illustrated to be suitable for real-time robust object tracking due to its low computational complexity.