Real-time 3D rotation smoothing for video stabilization
We propose two real-time motion smoothing algorithms for video stabilization using a pure 3D rotation motion model with known camera projection parameters. Both proposed algorithms aim at smoothing 3D rotation matrix sequences in a causal way. The first algorithm smooths the 3D rotation sequences in a way similar to 1st-order IIR filtering. The second algorithm uses sequential probabilistic estimation under a constant angular velocity model. These two algorithms are generalized from classical 2D motion smoothing algorithms. We exploit the manifold structure of the rotation matrices so that the proposed algorithms directly smooth the 3D rotation sequences on the manifold. In addition, we introduce a simple projection step in order to guarantee that no black borders intrude into the stabilized video frames. Experimental results show that the proposed algorithms are able to effectively stabilize video sequences and outperform their 2D counterparts with less jitter and distortion.