Stochastic Motion Coherency Analysis for Motion Vector Field Segmentation on Compressed Video Sequences
نویسندگان
چکیده
This contribution presents a stochastic analysis model, aiming at segmentation of the motion vector fields from compressed video sequences into regions of coherent motion. We propose a stochastic motion coherency model based on parametric affine motion. This model applies the 2-D GibbsMarkov random field to identify motion-coherent smoothcontour regions and corresponding motion model estimates. This stochastic approach inherently addresses the unreliability of such motion vector fields through the use of the motion coherency-based confidence analysis. The experimental optimisation algorithm based on this model demonstrates visually convincing results from two standard sequences.
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