## 4 Proposed Authoring System
### Creator Part
#### 4.1.2.2 Tracking Validation  
While applying the DCF-CSR tracker in 360\({}^{\circ}\) videos, many failures occurred due to object shape deformation, abrupt motions in stitched areas of the 360\({}^{\circ}\) video and the object moving out of one side of a frame and appearing on the other side, as presented in the results section. This module checked whether the tracker was tracking the right object or if it was lost. It increased the reliability of the tracker by ensuring that the object was tracked correctly. Tracking validation took place after every 10 frames during the whole 360\({}^{\circ}\) video, as shown in Figure 3 using Equation (1). In Equation (1), 'F\({}_{1}\)' denotes the current frame, 'F\({}_{0}\)' represents the frame when validation had to be applied again, 'F\({}_{\mathrm{V}}\)' represents the frame when validation was last applied, and 'hre' represents the threshold value of frames to be skipped. We tested the threshold value of 'thre' each from 1 to 20 for our system and found the best performance at 'thre = 10' as the threshold value for frame skip count in our scenario. Hence, in our proposed method, the predefined threshold value for 'thre' was set to 10. Validation was applied if and only if the condition shown in Equation (1) became true.  
\[\mathrm{F_{i}}=\mathrm{F_{0}}\text{, where }\mathrm{F_{0}}=\mathrm{F_{v}}+ \text{thre} \tag{1}\]  
This means a tracked object validated three times in a second. We decided to skip these frames because an object's appearance does not usually change much within a second. Moreover, applying validation after frames skip incurred less computational cost.  
The tracking validation module consisted of two main components: Feature extraction and feature matching. Both components worked mutually. We used the oriented fast and rotated BRIEF (ORB) feature descriptor, which is a fusion of the FAST key point detector [23] and BRIEF descriptor [24]. ORB was faster than the SIFT and SURF descriptors, as well as being better in matching performance [25]. We used ORB feature descriptors due to its fast performance and it incurred less computational cost to our proposed algorithm in feature matching. Moreover, for feature matching, we used the fast library for approximate nearest neighbors (FLANN)-based descriptor matcher [26]. It finds the nearest neighbors based on feature descriptors provided from two images. After performing feature matching, we kept only good matches based on the matching distance of the features between two images, as shown in Figure 4. The threshold for matching distance was 0.8 out of a maximum value of 1. We kept this value high to make the validation accurate and sensitive to avoid wrong object selection.  
The two images used for feature extraction and feature matching were decided based on the location of an object given by the DCF-CSR tracker. The first image was of the last saved appearance of the tracked object, while the second image corresponded to the latest bounding box given by the tracker. If the feature matching met the threshold, the second image would take the place of the first image and became the latest appearance of an object for future matching in 360\({}^{\circ}\) video frames. This process took place continuously with a difference of 10 frames, as shown in Equation (1). If the feature matching did not meet the threshold, we kept performing feature matching with the object's last  
Figure 4: Fast library for approximate nearest neighbors (FLANN)-based matching of oriented fast and rotated BRIEF (ORB) features.  
appearance for the next 60 frames with a skip of 10 frames. If it was still unable to meet threshold, the algorithm gave control to the object re-identification module with an input of the image with the object's last appearance.