  In the field of computer vision, a crucial task is the detection of motion
(also called optical flow extraction). This operation allows analysis such as
3D reconstruction, feature tracking, time-to-collision and novelty detection
among others. Most of the optical flow extraction techniques work within a
finite range of speeds. Usually, the range of detection is extended towards
higher speeds by combining some multiscale information in a serial
architecture. This serial multi-scale approach suffers from the problem of
error propagation related to the number of scales used in the algorithm. On the
other hand, biological experiments show that human motion perception seems to
follow a parallel multiscale scheme. In this work we present a bio-inspired
parallel architecture to perform detection of motion, providing a wide range of
operation and avoiding error propagation associated with the serial
architecture. To test our algorithm, we perform relative error comparisons
between both classical and proposed techniques, showing that the parallel
architecture is able to achieve motion detection with results similar to the
serial approach.
