  5G is designed to be an essential enabler and a leading infrastructure
provider in the communication technology industry by supporting the demand for
the growing data traffic and a variety of services with distinct requirements.
The use of deep learning and computer vision tools has the means to increase
the environmental awareness of the network with information from visual data.
Information extracted via computer vision tools such as user position, movement
direction, and speed can be promptly available for the network. However, the
network must have a mechanism to match the identity of a user in both visual
and radio systems. This mechanism is absent in the present literature.
Therefore, we propose a framework to match the information from both visual and
radio domains. This is an essential step to practical applications of computer
vision tools in communications. We detail the proposed framework training and
deployment phases for a presented setup. We carried out practical experiments
using data collected in different types of environments. The work compares the
use of Deep Neural Network and Random Forest classifiers and shows that the
former performed better across all experiments, achieving classification
accuracy greater than 99%.
