A polar model for fast object tracking in 360-degree camera images

The task of fast object tracking in polar images using emerging high-resolution 360-degree camera technology is presented in this paper. In this approach, when an arbitrary object has been selected in the first frame, the proposed method searches for the object in the next frames. This task is challenging when the video contains complexity which cannot be handled by common tracking methods. The main contribution of this paper uses polar object selection and color binary features to facilitate robust object tracking in 360-degree images. Using the proposed polar object selection method, each object is represented by a polar component and high performance of the tracking algorithm in terms of precision and speed is achieved. We evaluate the applicability of our approach on a new dataset containing more than 30000 frames of 360-degree images wherein high performance in challenging real-world scenarios is demonstrated. The proposed algorithm outperforms the related methods.