Camera relocalization is an important component in localization systems such as augmented reality or robotics when camera tracking loss occurs. It uses models built from known information of a scene. However, these models cannot perform in a dynamic environment which contains moving objects. In this paper, we propose an adaptive regression forest and apply it to our DynaLoc, a real-time camera relocalization approach from a single RGB image in dynamic environments. Our adaptive regression forest is able to fine tune and update continuously itself from evolving data in real-time. This is performed by updating a relevant subset of leaves, which gives uncertain predictions. The results of camera relocalization in dynamic scenes report that our method is able to address a large number of moving objects or a whole scene to gradually change in order to obtain high accuracy avoiding accumulation of error. Moreover, our method achieves results as accurate as the best state-of-the-art methods on static scenes dataset.