The main goal of the project is to design a new model that predicts regions of interest in 360$^{\circ}$ videos. The region of interest (ROI) plays an important role in 360$^{\circ}$ video streaming. For example, ROIs are used to predict view-ports, intelligently cut the videos for live streaming, etc so that less bandwidth is used. Detecting view-ports in advance helps reduce the movement of the head while streaming and watching a video via the head-mounted device. Whereas, intelligent cuts of the videos help improve the efficiency of streaming the video to users and enhance the quality of their viewing experience. This report illustrates the secondary task to identify ROIs, in which, we design, train, and test a hybrid saliency model. In this work, we refer to saliency regions to represent the regions of interest. The method includes the processes as follows: preprocessing the video to obtain frames, developing a hybrid saliency model for predicting the region of interest, and finally post-processing the output predictions of the hybrid saliency model to obtain the output region of interest for each frame. Then, we compare the performance of the proposed method with the subjective annotations of the 360RAT dataset.

翻译：本项目的主要目标是设计一种新模型，用于预测360°视频中的兴趣区域。兴趣区域在360°视频流传输中扮演着重要角色。例如，ROI可用于预测视口、为直播流智能裁剪视频等，从而减少带宽占用。提前检测视口有助于减少用户通过头戴设备流式传输和观看视频时的头部移动。而视频的智能裁剪则有助于提高向用户传输视频的效率，并提升其观看体验的质量。本报告阐述了识别ROI的次要任务，其中我们设计、训练并测试了一种混合显著性模型。在本工作中，我们通过显著性区域来表征兴趣区域。该方法包括以下流程：预处理视频以获取帧序列，开发用于预测兴趣区域的混合显著性模型，最后对混合显著性模型的输出预测进行后处理，以获得每帧的输出兴趣区域。随后，我们将所提方法的性能与360RAT数据集的主观标注结果进行了比较。