To deep, or not to deep, that is the question!

As in other fields of artificial intelligence and prior to the emergence of Deep Learning, especially deep neural networks, artificial vision research was focused on a traditional Machine Learning approach. The traditional machine learning approach relies on developers massaging the data to extract the most salient or significant aspects from the data they are dealing with; that is, time sequences of frames, or videos. In this case, both scientific research and application development have been centered around identifying the most significant image elements that would allow, for example, facial and body recognition of the people who appear in the images, tracking them from one frame to another, or classifying the vehicles that move through a given area. After extracting this meaningful data, statistical methods are then employed to transform the representation into a so-called "understanding" of the real visual environment by using clustering, support-vector machines (SVMs), and filtering algorithms (linear, non-linear, regression), among others. This means that the merits of any given application lie in how well researchers and developers are able to source and generate data from the raw processed frames and transform it into useful structured data.