engleski

Optical Flow Estimation over Extended Image Sequence

A number of proposed optical flow estimation algorithms for image motion analysis use only a few subsequent frames from an image sequence to perform calculation. Others, that use many frames, as is case in frequency and energy based, as well as in stochastic optical flow algorithms, are better suited for off-line work. In this work we present our approach in adopting a classic gradient optical flow algorithm for on-line work over extended image sequence. Our experiments on usual optical flow testing image sequences, consisting of few dozens of image frames show significant improvements in accuracy of estimated velocity flow fields and in speed of computation. This is achieved by modification and integration of three basic processing phases: preparation of a limited spatio-temporal volume, estimation of velocity flow fields and post filtering of the flow fields. In the preparation phase we use efficient spatio-temporal filters for image smoothing. In estimation phase a modified iterative solution that propagates results from previous calculation steps is used. Linear and nonlinear post filtering additionally improve optical flow estimates. Whole process is less sensitive to apperture problem and produces more accurate flow field estimates then original algorithm. It better copes with motion discontinuities as well. We evidence our achievements by a number of experiments on extended image sequences.

computer vision; optical flow; temporal refinement

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