## 4 Proposed Authoring System
### Creator Part
#### 4.1.2.3 Object Re-Identification  
This is a popular topic in the field of computer vision and its applications. Many algorithms have been proposed for object re-identification, such as [27]. Due to the 360\({}^{\circ}\) view, an object stays inside the frame, unlike normal NFOV videos, unless and until it goes out of the sight of the 360\({}^{\circ}\) camera. This module executed when the DCF-CSR tracker gave a failure message or when the tracking validation module rejected the tracked region in a 360\({}^{\circ}\) video based on knowledge about the object's last appearance. The DCF-CSR tracker mainly lost the object when it went out and appeared on the opposite side of the panoramic frame. Since 360\({}^{\circ}\) panoramic videos normally have very high frame resolution, it took a very long time to scan a whole frame for object re-identification. As a result, we used two main techniques to make object re-identification faster for 360\({}^{\circ}\) videos. The first technique was based on segmentation of the moving regions in a frame sequence. Whenever tracking failure occurred, we extracted the regions from the video where movements appeared by using one of the best techniques for real-time segmentation, called a MOG background subtractor [28]. It gave the moving regions as white pixels, whereas unchanged regions were black. The output image of the background subtractor had noise in it due to minute pixel changes. Therefore, noise removal was required to get only meaningful information from the resultant image. To remove the noise from the image and extract the moving objects from the 360\({}^{\circ}\) frame, we applied morphological operations. First, we applied erosion to the image with a very small kernel of size (1, 1). Erosion removed the white noise from the image. As erosion removed the white pixel noise, it also made the white regions very thin by decreasing the size of the foreground. Here, we needed to increase the thickness of the white pixels to get the exact moving regions with more information. For this purpose, we applied a dilate operation to the binary image to increase the white pixel area, as shown in Figure 5a. After applying the morphological operations, we drew contours around white pixels to extract the objects from the binary image. After getting the contours from the binary image, we had to extract moving objects from the original colored 360\({}^{\circ}\) frame. For this purpose, we applied a bitwise AND operation using the original image and binary image mask. In this way, we got the output image with moving objects as colored regions in it while the remaining area remained black (Figure 5b). After this step, we cropped the areas of moving regions based on the location of contours in the image (Figure 5c). These were considered as initial candidates for object selection. Based on the size of the tracked object in the last frame before losing track, we considered this size as a threshold for selecting the candidates for object selection. We determined the size of initial candidate objects based on their contour areas. Candidates that did not meet the size threshold were rejected at this stage. Then, the candidates from the section of extraction of moving objects were finalized to forward them to the next module. Figure 5: (**a**) Background subtraction and morphological operations, (**b**) bitwise AND, (**c**) initial candidate objects. The second technique for object re-identification was to restrict the search area within the 360\({}^{\circ}\) frame. This technique also produced candidates for object selection. Here, we restricted the area for object searching around the previous location of the object. We cropped a window around the central pixel coordinate of the object's previous location and performed searching for the desired object only in this area. One of the benefits of this technique was that it reduced the computational cost and kept the algorithm's processing fast. Objects did not move very quickly in successive frames, so it did not affect the tracking performance.