2006 年 64 巻 2 号 p. 395-403
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.
