A Principal Component Regression Strategy for Estimating Motion

V.V. Estrela, M.H. Da Silva Bassani, and J.T. de Assis (Brazil)


Motion estimation, principal component regression, and surveillance.


In this paper, we derive a principal component regression (PCR) method for estimating the optical flow between frames of video sequences according to a pel-recursive manner. This is an easy alternative to dealing with mixtures of motion vectors due to the lack of too much prior information on their statistics (although they are supposed to be normal). The 2D motion vector estimation takes into consideration local image properties. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.

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