  The device used in this work detects the objects over the surface of the
water using two thermal cameras which aid the users to detect and avoid the
objects in scenarios where the human eyes cannot (night, fog, etc.). To avoid
the obstacle collision autonomously, it is required to track the objects in
real-time and assign a specific identity to each object to determine its
dynamics (trajectory, velocity, etc.) for making estimated collision
predictions. In the following work, a Machine Learning (ML) approach for
Computer Vision (CV) called Convolutional Neural Network (CNN) was used using
TensorFlow as the high-level programming environment in Python. To validate the
algorithm a test set was generated using an annotation tool that was created
during the work for proper evaluation. Once validated, the algorithm was
deployed on the platform and tested with the sequence generated by the test
boat.
