Hierarchical Kalman-particle filter with adaptation to motion changes for object tracking

We present a new tracking method with improved efficiency and accuracy based on the subspace representation and particle filter. The subspace representation has been successfully adopted in tracking, e.g., the Eigen-tracking algorithm, and it has shown considerable robustness for tracking an object with changing appearance. Particle filters are widely used for a wide range of tracking problems since they can efficiently handle non-Gaussian and nonlinearity. Their combination has shown superior performance in terms of accuracy and robustness, but it suffers from the heavy computational load. Our tracking algorithm requires a significantly small number of particles while maintaining robustness and accuracy. We propose two methods in our tracking algorithm: first, we analyze object motion in a coarse-to-fine way and use hierarchical strategy to estimate it, in which the Kalman filter estimates global linear motion and the particle filter handles the local nonlinear motion, second, we give a more physically meaningful proposal distribution of the particle filter with consideration of the nature of motion. Experiments demonstrate the effectiveness of our tracking algorithm in real video sequences in which the target objects undergo rapid and abrupt motion. Furthermore, we provide quantitative comparisons between the existing tracking algorithm and the proposed tracking algorithm.