Data-driven saliency detection has attracted strong interest as a result of applying convolutional neural networks to the detection of eye fixations. Although a number of imagebased salient object and fixation detection models have been proposed, video fixation detection still requires more exploration. Different from image analysis, motion and temporal information is a crucial factor affecting human attention when viewing video sequences. Although existing models based on local contrast and low-level features have been extensively researched, they failed to simultaneously consider interframe motion and temporal information across neighboring video frames, leading to unsatisfactory performance when handling complex scenes. To this end, we propose a novel and efficient video eye fixation detection model to improve the saliency detection performance. By simulating the memory mechanism and visual attention mechanism of human beings when watching a video, we propose a step-gained fully convolutional network by combining the memory information on the time axis with the motion information on the space axis while storing the saliency information of the current frame. The model is obtained through hierarchical training, which ensures the accuracy of the detection. Extensive experiments in comparison with 11 state-of-the-art methods are carried out, and the results show that our proposed model outperforms all 11 methods across a number of publicly available datasets.

通过模拟人类观看视频时的记忆机制和视觉注意机制，我们提出了一种新颖高效的视频眼球凝视检测模型，通过在时间轴上组合记忆信息和在空间轴上的运动信息，存储当前帧的显著性信息来改善显著性检测性能，并通过分层训练获得模型，实验结果表明我们提出的模型在多个公开数据集上优于现有的11个最先进的方法。

SG-FCN: 一种基于运动和记忆的视频显著性检测深度学习模型