In this paper, we propose a novel approach for real-time 3-D tracking of object pose from a single camera. We formulate the 3-D pose tracking task in a Bayesian framework which fuses feature correspondence information from both previous frame and some selected key-frames into the posterior distribution of pose. We also developed an inter-frame motion inference algorithm which can get reliable inter-frame feature correspondences and relative pose. Finally, the maximum a posteriori estimation of pose is obtained via stochastic sampling to achieve stable and drift-free tracking. Experiments show significant improvement of our algorithm over existing algorithms especially in the cases of tracking agile motion, severe occlusion, drastic illumination change, and large object scale change.

In: IEEE Trans. CSVT

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