抄録
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.
