# Comparison of Tracking Techniques on 360-Degree Videos
## 2 Background
The MOSSE tracker calculates the minimum output sum of square error to find out the most possible location of the tracking object. The benefits of using a correlation filter make the MOSSE tracker more robust to the problems of scaling, rotation, deformation, and occlusion compared to traditional approaches. Also,MOSSE is more flexible than other correlation-filter-based trackers because the target is not required to be in the center of the image in the beginning of tracking. The TLD tracker, proposed by [PERSON] et al. [17], is mainly composed of three parts: a tracker, a learner, and a detector. The job of the tracker is to follow the target through consecutive frames; the learner relies on a P-expert and an N-expert to estimate misdetection and false alarm, respectively, then updates the detector. The detector locates potential targets according to an appearance model, feeds the outputs to the learner, and corrects the tracker if necessary. The TLD tracker is well known for its ability of failure recovery at the expense of instability. Compared to other online trackers struggling with the problem of accumulating errors, the combination of tracking and detecting modules makes the TLD tracker more reliable for long-term tracking. The KCF tracker, proposed by [PERSON] et al. [18], extends the MOSSE concept and takes advantage of overlapping regions in multiple positive samples. The abundant data is computed in Fourier domain to increase the learning speed. The KCF tracker emphasizes the importance of the negative samples and tends to use more samples for better training. To this end, a cyclic shift is applied to generate more samples from each important sample. The characteristic of circulant matrices for regression samples is utilized to speed up the computation. Also, kernel tricks are exploited to deal with the problem of nonlinear regression. Instead of scanning through raw pixels, the KCF tracker extracts the Histogram of Gradient (HoG) features to improve the accuracy of tracking. The Generic Object Tracking Using Regression Networks (GOTURN) tracker, proposed by [PERSON] et al. [19], adopts an offline dataset to train a Convolutional Neural Network (CNN) model in advance. Then, it relies on the generated model for online tracking. The process of pretraining takes advantage of readily available information in offline datasets to learn both target appearance and motion relationship. Without the requirement to update CNN weights in run-time, the GOTURN tracker has another significant advantage of online tracking speed. Although it is not necessary to include specific tracking targets in the dataset for pretraining, the GOTURN tracker tends to favor objects in the training set over objects that are not in the training set. A potential issue of the GOTURN tracker is the quality of the pretrained model that can seriously affect the performance of the online tracking process. The Channel and Spatial Reliability Tracker (CSRT), proposed by [PERSON] et al. [20], is based on the Discriminative Correlation Filter (DCF) algorithm. It improves the DCF tracker by introducing spatial and channel reliability. The spatial reliability map is used to find out the optimal filter size. The ability to adjust filter size makes the CSRT tracker better than the traditional DCF algorithm by excluding unrealistic samples. Another benefit from the spatial reliability map is its ability to handle nonrectangular targets. The channel reliability is measured to weigh the importance of each channel filter, then combine them to get the final response map. Using only the HoGs and Colorname standard feature sets, the CSRT tracker can achieve an impressive accuracy with real-time speed.