Smartly selecting and preparing application specific training data

In this section, we will discuss how much training samples are needed according to the situational context and highlight some important aspects when preparing your annotations on the positive training samples.

Let's start by defining the principle of object categorization and its relation to training data, which can be seen in the following figure:

An example of positive and negative training data for an object model

The idea is that the algorithm takes a set of positive object instances, which contain the different presentations of the object you want to detect (this means object instances under different lighting conditions, different scales, different orientations, small shape changes, and so on) and a set of negative object instances, which contains everything that you do not want to detect with your model. Those are then smartly combined into an object model and used to detect new object instances in any given input...