**5. Conclusions**

Although object recognition is a very active area of research, it is still considered an overly complex task due to the following difficulties: 1. Objects of the same class with high variability in appearance. A class of objects can integrate elements with variations in shape, color, and texture. In addition, multiple factors such as position, lighting, and occlusions, among others, can increase these differences. 2. The lack of reference images for the training phase of the classifier. The available data are generally not enough to cover the variability in appearance of objects. Furthermore, there may be significant differences in the conditions of training and system operation.

This research has placed special emphasis on the study of HOG algorithms for the feature extraction stage. This is because HOG has demonstrated that using normalized representations of objects can generate representations that provide discriminative information from the objects in an image. Furthermore, since HOG operates on local cells, it is invariant to geometric and photometric transformations as well as to changes in background and object position. On the other hand, it seeks to exploit the well-known features of the MLP to solve the problem that occurs when the limited data available during training are generally not enough to cover the variability in the appearance of objects.

It is important to emphasize that the proposed improvement in the step of calculating the HOG algorithm gradient reduces the rate of false positives. It was also demonstrated that HOG can accurately represent different objects and offers good performance in multiclass applications. Finally, we show that a classifier that uses a neuronal approach is an excellent complement to a HOG-based feature extractor.

It is the intention of this working group to use the proposed system in autonomous systems applications through its modeling on reconfigurable logic.
