Abstract
This paper proposes an efficient optical flow filtering method for video sequences. Motivated by the observation that motions in videos have strong temporal coherence, we use Kalman filtering to exploit this characteristic for more accurate flow fields. In the proposed system, pixel's motion flow is formulated as a time-variant state vector and optimally estimated by Kalman filter according to the noise level, which is evaluated using flow's temporal derivative, spatial gradient and matching error. Experiments on MPI Sintel video dataset demonstrate that the temporal coherence employed during Kalman filtering has the advantage of more consistent results, and can contribute to the state-of-the-art methods.
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