Deep learning for object tracking in 360 degree videos

Object tracking is used to locate the position of an object over a period of time using the association of an object of interest over consecutive frames. In the last years, several methods were proposed to track objects in rectangular videos. This paper presents is an object tracking method within 360-degree videos using a state-of-the-art tracking-by-detection paradigm. This method uses two trackers namely Kalman filter and Lucas-Kanade methods to handle challenges in the 360-degree videos. The proposed method uses a deep learning object detector for extraction of prior information of the object of interest. The information is then used, to track the object of interest using a combination of the two trackers of the Kalman filter and Lucas-Kanade. The experiments show that this combination improves the tracker stability.