What if you could take a picture of your face and have a neural network arbitrarily change how you look? This is exactly what the IcGAN does using the power of Generative Adversarial Networks. It doesn't apply to faces only, but to whatever image dataset you train with.
My teammates and I analyze different approaches to effectively monitor traffic. Considering the limitations of the real world (e.g. camera jittering, dynamic background), we use computer vision techniques to track vehicles and estimate their speed with an RGB camera.
We built a detector and classifier able to recognize 15 different types of traffic signs with 81.05% precision and 75.68% F1-Score. We experimented with hand-crafted features and neural networks.
Given an aerial video of a terrain captured with a UAV equipped with a sensor, I learn and explore different techniques to accurately represent the map where the UAV has been.