## 1 Introduction  
Virtual reality (VR) and 360\({}^{\circ}\) videos mutually deliver an immersive experience of visual content to users. As it represents the whole world, 360\({}^{\circ}\) images hold more information than normal images. VR has increased user interest in 360\({}^{\circ}\) visual content by giving them a feeling of being present in the content [1]. Social media platforms such as Facebook and YouTube are also now open to uploads of 360\({}^{\circ}\) content [2]. Hence, consumer influence on entertainment through 360\({}^{\circ}\) videos is increasing day by day. One main reason is that they get a lot of interesting information to explore in a single visual content source. Promotion of 360\({}^{\circ}\) visual content is rising, but at the same time many challenges are faced while watching a 360\({}^{\circ}\) video in VR. One of these challenges is the limited field of view (FOV) in head-mounted displays (HMD). In VR, a user can only focus on a specific region at a time due to limited FOV. Consequently, there is a huge possibility of missing interesting and important information while watching 360\({}^{\circ}\) visual content in VR. To overcome this problem, there is a concept of providing a narrative for watching a 360\({}^{\circ}\) video in VR. The narrative consists of information about a region of interest (ROI) during the whole 360\({}^{\circ}\) video. The narrative for the 360\({}^{\circ}\) video shapes the user's actual experience as it guides the user towards an ROI. An ROI depends on some interesting object or specific area in a video. There can be multiple ROIs at a time in a 360\({}^{\circ}\) video. Each viewer can focus on different ROIs in VR based on personal interest. Hence, there is a huge possibility of watching a single 360\({}^{\circ}\) video in different ways, resulting in various experiences based on the visual information watched in VR. By recording the users' experiences in VR, multiple narratives for a single 360\({}^{\circ}\) visual content source can be generated. Later, these experiences can be transferred to new viewers using focus assistance techniques in VR. It helps users focus on ROI at the right time, which saves them from missing important information in visual content. Moreover, 360\({}^{\circ}\) video content holds many interesting objects in it. Based on personal interest, viewers can choose various objects for watching in a panoramic video. Therefore, it is possible to generate a narrative for each interesting object by tracking it in the whole 360\({}^{\circ}\) video. To create a narrative based on an interesting object, it is required to track that object in the rest of the frames of a panoramic video. It is very difficult to select an object manually in all the frames of a video. Therefore, a fast and robust tracker is required to track the object of the user's interest in the video. There are many existing object trackers for normal videos. When these trackers are applied to a 360\({}^{\circ}\) panoramic video, many difficulties arise. The most noticeable problem is the movement of the object out of one side of the horizontal margin of the panoramic frame and reappearing on the other side (Figure 1a,b). Moreover, deformation of the object's shape occurs in panoramic images, which results in the loss of the object. In such scenarios, existing object trackers were not robust enough to re-identify and track objects in 360\({}^{\circ}\) panoramic videos. Moreover, the author of the narrative can be interested in any kind of object within a 360\({}^{\circ}\) video. Therefore, it is important to enable a user to create a narrative based on tracking of an arbitrary/unknown object. In the case of unknown/arbitrary object tracking in a 360\({}^{\circ}\) video, model-based and offline methods were unable to perform well because of a lack of prior information about each desired object due to deformations in the object's shape during the video [3]. Figure 1: (**a**) Plane cockpit at far right of panoramic frame.