Variation in the Local Motion Statistics of Real-Life Optic Flow Scenes

Optic flow motion patterns can be a rich source of information about our own movement and about the structure of the environment we are moving in. We investigate the information available to the brain under real operating conditions by analyzing video sequences generated by physically moving a camera through various typical human environments. We consider to what extent the motion signal maps generated by a biologically plausible, two-dimensional array of correlation-based motion detectors (2DMD) not only depend on egomotion, but also reflect the spatial setup of such environments. We analyzed the local motion outputs by extracting the relative amounts of detected directions and comparing the spatial distribution of the motion signals to that of idealized optic flow. Using a simple template matching estimation technique, we are able to extract the focus of expansion and find relatively small errors that are distributed in characteristic patterns in different scenes. This shows that all types of scenes provide suitable motion information for extracting ego motion despite the substantial levels of noise affecting the motion signal distributions, attributed to the sparse nature of optic flow and the presence of camera jitter. However, there are large differences in the shape of the direction distributions between different types of scenes; in particular, man-made office scenes are heavily dominated by directions in the cardinal axes, which is much less apparent in outdoor forest scenes. Further examination of motion magnitudes at different scales and the location of motion information in a scene revealed different patterns across different scene categories. This suggests that self-motion patterns are not only relevant for deducing heading direction and speed but also provide a rich information source for scene structure and could be important for the rapid formation of the gist of a scene under normal human locomotion.