Visual Object Tracking is the computer vision problem of estimating a target trajectory in a video given only its initial state. A visual tracker often acts as a component in the intelligent vision systems seen in for instance surveillance, autonomous vehicles or robots, and unmanned aerial vehicles. Applications may require robust tracking performance on difficult sequences depicting targets undergoing large changes in appearance, while enforcing a real-time constraint. Discriminative correlation filters have shown promising tracking performance in recent years, and consistently improved state-of-the-art. With the advent of deep learning, new robust deep features have improved tracking performance considerably. However, methods based on discriminative correlation filters learn a rigid template describing the target appearance. This implies an assumption of target rigidity which is not fulfilled in practice. This thesis introduces an approach which integrates deformability into a stateof-the-art tracker. The approach is thoroughly tested on three challenging visual tracking benchmarks, achieving state-of-the-art performance.