Monocular optical flow for real-time vision systems

This paper introduces a monocular optical flow algorithm that has been shown to perform well at nearly real-time frame rates (4 FPS on a 100 MHz SGI Indy workstation), using natural image sequences. The system is completely bottom-up, using pixel region-matching techniques. A coordinated gradient descent method is broken down into two stages: pixel region matching error measures are locally minimized and flow field consistency constraints apply nonlinear adaptive diffusion, causing confident measurements to influence their less confident neighbors. Convergence is usually accomplished with one iteration for an image frame pair. Temporal integration predicts upcoming flow fields. The algorithm is designed for flexibility: large displacements are tracked as easily as sub-pixel displacements, and higher-level information can feed flow field predictions into the measurement process.

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