Object tracking is a recurrent problem in the current world: video surveillance, autonomous driving, military scenarios, just to name a few. Several methods have been applied to this problem, such as algorithms based on hand crafted features, however deep learning methods have shown better results, due to their ability to capture complex transformations and to scale with the number of training examples. Nowadays, most of the state-of-the-art trackers use a convolutional neural network, whereas only few of them combine both a convolutional and recurrent neural network to both capture the visual information about the tracked object and remember its appearance over time.