  Finding the best mathematical equation to deal with the different challenges
found in complex scenarios requires a thorough understanding of the scenario
and a trial and error process carried out by experts. In recent years, most
state-of-the-art equation discovery methods have been widely applied in
modeling and identification systems. However, equation discovery approaches can
be very useful in computer vision, particularly in the field of feature
extraction. In this paper, we focus on recent AI advances to present a novel
framework for automatically discovering equations from scratch with little
human intervention to deal with the different challenges encountered in
real-world scenarios. In addition, our proposal can reduce human bias by
proposing a search space design through generative network instead of
hand-designed. As a proof of concept, the equations discovered by our framework
are used to distinguish moving objects from the background in video sequences.
Experimental results show the potential of the proposed approach and its
effectiveness in discovering the best equation in video sequences. The code and
data are available at:
https://github.com/carolinepacheco/equation-discovery-scene-analysis
