Individuals motion models based on probabilistic distribution profiles

The motion models of individuals should mathematically describe, as well as possible, the movements executed by an individual when he is walking, running or even stopping. There are some references in the literature with respect to individuals motion models, but most of them are based on image processing. In this paper, we proposed three probabilistic distribution profiles that can model the heading angle of an individual trajectory without relying on image processing. These profiles are based on probability density functions and cumulative distribution functions. Objectivity tests and standard deviation analysis were made in order to verify each profile behavior.