# Comparison of Tracking Techniques on 360-Degree Videos
## 1 Introduction  
Nowadays, 360-degree videos are becoming more and more popular. Omnidirectional cameras, also called 360-degree cameras, are widely available and more lightweight, and can even be installed on drones [1]. They are useful for recording indoor or outdoor activities to cover views in all perspectives. Rendering 360-degree videos on a virtual reality headset can provide immersive experience for users of education, entertainment, and tourism [2]. For augmented reality applications using 360-degree videos, a common request is to register a virtual tag to a physical target. As shown in Figure 1, a virtual billboard marked in red color must follow its corresponding physical target over time. For this purpose, automatic tracking of a specific target in 360-degree videos is highly desirable. Therefore, we explore the characteristics of 360-degree videos and compare the performance of existing tracking techniques on 360-degree videos. A 360-degree video consists of a sequence of 360-degree images with a fixed interval of time. Each 360-degree image is a panorama either captured by an omnidirectional camera or combined by multiple cameras to cover the complete horizontal field of view (i.e., 360-degree FOV). As shown in Figure 1, a 360-degree image is typically flattened in an equirectangular format in that longitude lines are projected to vertical straight lines of constant spacing. Similarly, latitude lines are mapped to horizontal straight lines of constant spacing. Due to the natures of the immersiveness and full field of view, 360-degree videos are ideal to be adopted in virtual reality applications. However, the interaction ways during playback of 360-degree videos are quite limited. Users cannot walk within the scene in 360-degree videos, that is, 3D translation is not allowed besides the passive motion caused by a moving camera. Nevertheless, users can freely select their point of view during playback of 360-degree videos (i.e., 3D rotation is possible). Conveniently, a 360-degree video can be viewed via an ordinary web browser in that a user can pan around by clicking and dragging. Alternatively, a 360-degree video can be observed via a head-mounted display (HMD) in that a user can pan around simply by rotating his head. In ordinary monoscopic videos, real-time tracking techniques rely on either offline datasets or online learning to train an appearance model, then apply the trained model to track potential targets frame by frame. However, tracking algorithms designed for ordinary videos may not perform well on 360-degree videos with their unique characteristics [3, 4]. For example, occlusion problems are almost unavoidable in panoramic 360-degree videos. Moreover, an object may disappear from the left but reappear on the right border, or disappear from the top then reappear on the bottom border in 360-degree videos. Nonrigid deformation is obvious on 360-degree videos in an equirectangular format. Lighting and scale changes happen frequently in 360-degree videos due to continuous change of viewpoints. Camera shakiness is another common problem in aerial 360-degree videos captured by drones. Trackers designed for ordinary videos tend to confuse or even lose the tracking target under these circumstances in 360-degree videos. To this end, [PERSON] et al. [5] adapted the Kernelized Correlation Filter (KCF) tracking algorithm to work on 360-degree videos. [PERSON] et al. [6] modified the Track-Learn-Detection (TLD) tracker to fit the needs of 360-degree videos. Nevertheless, a thorough evaluation of state-of-the-art tracking algorithms on 360-degree videos is still missing. For this purpose, we implement eight popular tracking techniques and evaluate their performance on 360-degree videos. To make a fair comparison of both quality and time of tracking, we adopt the default parameters of these trackers in an open-sourced library called OpenCV. The experimental results are analyzed to reveal the pros and cons of these tracking algorithms on 360-degree videos.