Motion blur is pervasive in object tracking, especially in applications such as unmanned aerial vehicles or pods. However, the focus of tracking research has been on generic visual tracking rather than specific scenarios, such as motion blur, which degrades the performance in these scenarios. In this work, we propose an effective method for tracking in motion blur by employing the framework of D3S (a discriminative single shot segmentation tracker). IQA (image quality assessment) and deblurring components are both introduced into the basic D3S framework to enhance context patch, which improves the tracking accuracy in blurred target tracking. Extensive experiments demonstrate that our tracker can robustly track objects, not only in blurred videos but also in other challenging scenes.