Salient Object Detection in Video using Deep Non-Local Neural Networks
This work addresses the problem of detecting salient objects in video for computer vision applications, representing an incremental improvement over existing methods.
The paper tackled video salient object detection by introducing a deep non-local neural network architecture that captures global dependencies to exploit appearance and motion features, and it outperformed state-of-the-art methods on DAVIS and FBMS datasets.
Detection of salient objects in image and video is of great importance in many computer vision applications. In spite of the fact that the state of the art in saliency detection for still images has been changed substantially over the last few years, there have been few improvements in video saliency detection. This paper investigates the use of recently introduced non-local neural networks in video salient object detection. Non-local neural networks are applied to capture global dependencies and hence determine the salient objects. The effect of non-local operations is studied separately on static and dynamic saliency detection in order to exploit both appearance and motion features. A novel deep non-local neural network architecture is introduced for video salient object detection and tested on two well-known datasets DAVIS and FBMS. The experimental results show that the proposed algorithm outperforms state-of-the-art video saliency detection methods.