Salient Object Detection in Video using Deep Non-Local Neural Networks

Abstract

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.

arXiv commentSubmitted to Journal of Visual Communication and Image Representation

References in corpus (16)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Attention U-Net: Learning Where to Look for the Pancreas
• Salient Object Detection: A Benchmark
• Deeply supervised salient object detection with short connections
• Video Salient Object Detection via Fully Convolutional Networks
• Deep Visual Attention Prediction
• DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
• Review of Visual Saliency Detection with Comprehensive Information
• Non-Local Recurrent Network for Image Restoration
• Visual Saliency Based on Multiscale Deep Features
• Deep Contrast Learning for Salient Object Detection
• Video Salient Object Detection Using Spatiotemporal Deep Features
• Video Summarization with Attention-Based Encoder-Decoder Networks
• DifNet: Semantic Segmentation by Diffusion Networks
• Video Saliency Detection by 3D Convolutional Neural Networks
• Region-Based Multiscale Spatiotemporal Saliency for Video