Object Tracking with Robust Kalman Filter Based on Convex Optimization

Abstract

Tracking moving objects with high accuracy is an important problem in fields of robot vision, video signal processing and pattern recognition. One of the widely used object tracking methods is Kalman filter. Conventional Kalman filter has a basic assumption that the noise obeys Gaussian distribution. However, in practice, the observed data also suffers from non-stationary sparse noise, which may cause performance deterioration for conventional Kalman filter. In this paper, we propose a robust Kalman filter based on convex optimization to remove not only the Gaussian noise but also such kind of sparse noise. Formulated as a convex optimization problem, the robust Kalman filter can be solved efficiently by Interior Point Method. Numerical results show that the proposed method is robust against sparse noise and achieves better performance while tracking objects under sparse noisy condition.