We present the results of a computer vision system intended to
describe in real time the trajectories of moving objects in a
variety of speeds. The developed computing mechanism includes
channel processing for instant position and velocity estimation,
while trajectory plotting is made by combining direction and speed
of movement. Retinal ganglion cell-like computational elements are
randomly distributed thorough the input image, being
direction-selective, allowing lateral interactions among them to
correct peep-hole effects in movement analysis, presenting a
variable degree of overlapping of their receptive fields (RF). We
show results and errors of the working system when all parameters
of cells are varied. One unexpected characteristic is that of the
degradation of results in trajectory estimation when the size of
receptive fields or the number of receptive fields, i.e. of
computing cells, increase. In order to get good results for a
variety of moving object speeds we have to reach a compromise
between size and number of cells. This effect has interesting
implications when translated back to the biological system which
originally inspired the design of the computational method and
could be used as a rationale to explain the distribution,
morphological characteristics and number of movement detecting
neurons.