Viewing Bias Matters in 360 Videos Visual Saliency Prediction

360 degrees video has been applied to many areas such as immersive contents, virtual tours, and surveillance systems. Compared to the field of view prediction on planar videos, the explosive amount of information contained in the omni-directional view on the entire sphere poses an additional challenge in predicting high-salient regions in 360 degrees videos. In this work, we propose a visual saliency prediction model that directly takes 360 degrees video in the equirectangular format. Unlike previous works that often adopted recurrent neural network (RNN) architecture for the saliency detection task, in this work, we utilize 3D convolution to a spatial-temporal encoder and generalize SphereNet kernels to construct a spatial-temporal decoder. We further study the statistical properties of viewing biases present in 360 degrees datasets across various video types, which provides us with insights into the design of a fusing mechanism that incorporates the predicted saliency map with the viewing bias in an adaptive manner. The proposed model yields state-of-the-art performance, as evidenced by empirical results over renowned 360 degrees visual saliency datasets such as Salient360!, PVS, and Sport360.