In this paper, we propose a novel deep learning based video saliency prediction method, named DeepVS. Specifically, we establish a large-scale eye-tracking database of videos (LEDOV), which includes 32 subjects’ fixations on 538 videos. We find from LEDOV that human attention is more likely to be attracted by objects, particularly the moving objects or the moving parts of objects. Hence, an object-to-motion convolutional neural network (OM-CNN) is developed to predict the intra-frame saliency for DeepVS, which is composed of the objectness and motion subnets. In OM-CNN, cross-net mask and hierarchical feature normalization are proposed to combine the spatial features of the objectness subnet and the temporal features of the motion subnet. We further find from our database that there exists a temporal correlation of human attention with a smooth saliency transition across video frames. We thus propose saliency-structured convolutional long short-term memory (SS-ConvLSTM) network, using the extracted features from OM-CNN as the input. Consequently, the inter-frame saliency maps of a video can be generated, which consider both structured output with center-bias and cross-frame transitions of human attention maps. Finally, the experimental results show that DeepVS advances the state-of-the-art in video saliency prediction.
@cite_42 is a public video database containing eye-tracking data of 12 uncompressed YUV videos, which are frequently used as the standard test set for video compression and processing algorithms. Each video is in the CIF resolution ( @math ), and is with 3-10 seconds at a frame rate of 30 fps. All eye-tracking data were collected, when 15 non-expert subjects were free viewing all 12 videos twice.
@cite_42
1967508848
This correspondence describes a publicly available database of eye-tracking data, collected on a set of standard video sequences that are frequently used in video compression, processing, and transmission simulations. A unique feature of this database is that it contains eye-tracking data for both the first and second viewings of the sequence. We have made available the uncompressed video sequences and the raw eye-tracking data for each sequence, along with different visualizations of the data and a preliminary analysis based on two well-known visual attention models.