# Comparison of Tracking Techniques on 360-Degree Videos
## 2 Background  
With the advance of virtual reality technology in the past few years, 360-degree images and videos have become a blooming research topic. [PERSON] and [PERSON] [7] designed and evaluated 360-degree hypervideos that allow users to explore and navigate through links. [PERSON] et al. [8] adopted 360-degree interactive video to create evaluation scenarios where users can select their point of view during playback. [PERSON] et al. [9] used 360-degree videos as a learning tool and analyzed the effects of immersiveness of three devices: a smartphone, a Google Cardboard, and an Oculus Rift. [PERSON] et al. [10] compared three interactive ways for 360-degree video playback: remote control, head orientation, and hand gesture. [PERSON] et al. [11] presented an automatic approach to generate spatial audio for panorama images based on object detection and action recognition. Giving the initial position of an unknown object, the purpose of tracking is to locate the object in successive frames of a video. [PERSON] [12] proposed a method for controlling motions of a virtual partner character based on a performance-capturing process using multiple inertial measurement units (IMUs). Instead of relying on IMUs for human tracking, this paper focuses on vision-based methods for unknown object tracking. Among all the existing online visual tracking algorithms, we choose eight modern and popular trackers for evaluation and comparison on 360-degree videos. The Boosting tracker, proposed by [PERSON] et al. [13], is an online version of the AdaBoost feature selection algorithm. The online boosting algorithm maintains a global classifier pool of weak classifiers with multiple selectors. Each new training sample is used to update each weak classifier in the pool. A cascade system initializes the first selector with the current sample's importance, selects the best weak classifier with the least error, and passes the estimated importance to the next selector until all selectors have been updated. In the end, a strong classifier is chosen from the best weak classifiers, and the worst weak classifier is replaced with a random one. The Boosting tracker utilizes the initial target area in the current frame as a positive example, and exploits other areas with the same size around the target as negative examples. Then, the online-trained classifier searches the neighborhood for potential targets in the next frame. The Boosting tracker can handle temporary occlusions as well as complex backgrounds. The Multiple Instance Learning (MIL) tracker, proposed by [PERSON] et al. [14], extends the online boosting algorithm by using a set of image patches (called a bag) instead of a single sample for training. A bag containing at least one positive example is called a positive bag, otherwise it is called a negative bag. The MIL tracker collects lots of small image patches centered at the tracking object as potential positive bags, and chooses the best one to be the positive example. This strategy not only prevents the MIL tracker from losing important information but also avoids the mislabeling problem. The MedianFlow tracker, proposed by [PERSON] et al. [15], is a bidirectional approach that combines forward and backward tracking. The forward and backward consistency is analyzed as a quality measure to assist the tracking. The MedianFlow tracker constructs both forward and backward trajectories at each time instant, and their corresponding errors are estimated. The trajectory with the minimum forward-backward error is chosen as the candidate for the succeeding tracking. As a result, the MedianFlow tracker is more reliable to follow objects with consistent movement. The Minimum Output Sum of Squared Error (MOSSE) tracker, proposed by [PERSON] et al. [16], is a tracker based on correlation filters. It achieves high efficiency by computing correlation in time domain. The MOSSE filter improves the ASEF filter to overcome the potential overfitting problem.