Object tracking is the task of estimating over time the state of a single or multiple objects based on noisy measurements received from one or several sensors. The field of object tracking spans over several application domains ranging from military radar systems and sensor fusion approaches, to today's computer vision tracking methods employed in consumer electronics and surveillance systems. It also plays a substantial role in autonomous driving. In recent years, the use of deep neural networks has spiked in various fields, due to their impressive performance in detection and classification tasks. This aspect also makes these methods applicable to object tracking. Therefore, the aim of this survey is to give the reader a brief yet comprehensive start into the widespread field of object tracking with a focus on the latest deep-learning based extensions and approaches. At first, traditional non-deep tracking systems are briefly reviewed and a generic model of the individual components of such systems is introduced. Based on this structure the representative deep-based tracking applications in the literature are classified and presented.