Segmentação de movimento por fluxo ótico

Abstract

Motion Perception is an essential feature for the survival of several species. In nature, it is through motion that a prey perceives the predator and is able to decide which direction to escape, and the predator detects the presence of a prey and decides where to attack. The Human Visual System is more sensitive to motion than to static imagery, and it is able to separate motion information due to egomotion from that due to an animated object in the environment. The Ecological Theory of Gibson (1979) provides a basis for understanding how this processes of perception occurs, and leads to the concept of what we call the vector field of Optical Flow, through which computational motion is represented. The main objective of this work is to try to reproduce computationally this behaviour, for possible applications in autonomous navigation and video processing with unknown self-motion. For this, we use some Optical Flow estimation techniques, as those proposed by Lucas and Kanade (1981) and Farneback (1994). At first, we assess the possibility of using a statistical technique of blind source separation, the so-called Independent Component Analysis, based on the work of Bell and Sejnowski (1997), which demonstrates that this technique, when applied to imagery, provides edge filters. Then, we assess the use of the Focus of Expansion to translational motion. Experimental results show the second approach, using the Focus of Expansion, is more viable than through Independent Component Analysis.