Biological Motion Recognition Using a MT-like Model

We propose a bio-inspired system for biological motion recognition in image sequences. Our system has two main contributions. We propose a bio-inspired spiking VI model that transforms a video sequence into spikes train according to local motion detectors. The motion detectors are directionally spatial-temporal filters properly tuned for a certain range of velocity. At the same time we propose a method to obtain a histogram map representation for the velocity distribution of VI output. This histogram map acts as a MT-like model containing the spatial-temporal information of an event. We also propose a distance between histogram maps to realize motion categorization. In order to evaluate the performance of our approach, we ran our system in Giese database which contains 40 sequences and two actions, walk and march. The results reveal that motion categorization can be reliably estimated from the analysis of spike trains together with a coarse estimation of their spatial position