One of the things augmented reality depends on is object tracking, which is a problem classically found in cinematography and security. However, the algorithms designed for the classical application are often too expensive computationally or too complex to run on simpler mobile hardware. One of the methods to do object tracking is with a trained neural network, this has already led to great results but is unfortunately still running into some of the same problems as the classical algorithms. For this reason a neural network designed specifically for object tracking on mobile hardware needs to be developed. This thesis will propose two di erent neural networks designed for object tracking on mobile hardware. Both are based on a siamese network structure and methods to improve their accuracy using filtering are also introduced. The first network is a modified version of “CNN architecture for geometric matching” that utilizes an a ne regression to perform object tracking. This network was shown to underperform in the MOT benchmark as-well as the VOT benchmark and therefore not further developed. The second network is an object detector based on “SqueezeDet” in a siamese network structure utilizing the performance optimized layers of “MobileNets”. The accuracy of the object detector network is shown to be competitive in the VOT benchmark, placing at the 16th place compared to trackers from the 2016 challenge. It was also shown to run in real-time on mobile hardware. Thus the one shot object detection network used for a tracking application can improve the experience of augmented reality applications on mobile hardware.