Viewing Bias Matters in 360° Videos Visual Saliency Prediction
Journal
IEEE Access
Journal Volume
11
Date Issued
2023-01-01
Author(s)
Abstract
360° 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° videos. In this work, we propose a visual saliency prediction model that directly takes 360° 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° 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° visual saliency datasets such as Salient360!, PVS, and Sport360.
Subjects
360° videos | Convolutional neural networks | Decoding | Deep learning | deep learning | Feature extraction | Predictive models | Three-dimensional displays | Videos | viewing bias | Visual saliency prediction | Visualization
SDGs
Type
journal article