Robust Auxiliary Particle Filter with an Adaptive Appearance Model for Visual Tracking

Abstract

The algorithm proposed in this paper is designed to solve two challenging issues in visual tracking: uncertainty in a dynamic motion model and severe object appearance change. To avoid filter drift due to inaccuracies in a dynamic motion model, a sliding window approach is applied to particle filtering by considering a recent set of observations with which internal auxiliary estimates are sequentially calculated, so that the level of uncertainty in the motion model is significantly reduced. With a new auxiliary particle filter, abrupt movements can be effectively handled with a light computational load. Another challenge, severe object appearance change, is adaptively overcome via a modified principal component analysis. By utilizing a recent set of observations, the spatiotemporal piecewise linear subspace of an appearance manifold is incrementally approximated. In addition, distraction in the filtering results is alleviated by using a layered sampling strategy to efficiently determine the best fit particle in the high-dimensional state space. Compared to existing algorithms, the proposed algorithm produces successful results, especially when difficulties are combined.