Physical Sciences and Mathematics Commons^™

Estimating Heading From Optic Flow With Neural Networks, Natalie T. Maus

Honors Theses

Humans have a remarkable ability to estimate their direction of self-motion, or heading, based on visual input stimulus (optic flow). Machines, on the other hand, have a difficult time with this task, especially when flow is introduced that is inconsistent with the motion of the observer. For example, when moving objects enter the field of view, their motion provides inconsistent flow data which often disrupts heading estimates of current heading estimation models. We investigate the ability of neural networks to estimate heading from optic flow data and the limitations of these models when different variations of inconsistent flow are introduced. …