Following the gaze of people inside videos is an important signal for understanding people and their actions. In this paper, we present an approach for following gaze across views by predicting where a particular person is looking throughout a scene. We collect VideoGaze, a new dataset which we use as a benchmark to both train and evaluate models. Given one view with a person in it and a second view of the scene, our model estimates a density for gaze location in the second view. A key aspect of our approach is an end-to-end model that solves the following sub-problems: saliency, gaze pose, and geometric relationships between views. Although our model is supervised only with gaze, we show that the model learns to solve these subproblems automatically without supervision. Experiments suggest that our approach follows gaze better than standard baselines and produces plausible results for everyday situations.
Although related, gaze-following and free-viewing saliency refer to different problems. In gaze-following, we predict the location of the gaze of an observer in the scene, while in saliency we predict the fixations of an external observer free-viewing the image. Some authors have used gaze to improve saliency prediction, such as in @cite_3 . Furthermore, @cite_1 showed how gaze prediction can improve state-of-the-art saliency models. Although our approach is not intended to solve video saliency neither is using video as input, we believe it is worth mentioning some works learning saliency for videos such as @cite_8 @cite_27 @cite_6 .
@cite_8 @cite_1 @cite_6 @cite_3 @cite_27
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Data-driven saliency has recently gained a lot of attention thanks to the use of convolutional neural networks for predicting gaze fixations. In this paper, we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a convolutional long short-term memory that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. In addition, to tackle the center bias typical of human eye fixations, our model can learn a set of prior maps generated with Gaussian functions. We show, through an extensive evaluation, that the proposed architecture outperforms the current state-of-the-art on public saliency prediction datasets. We further study the contribution of each key component to demonstrate their robustness on different scenarios. The purpose of this paper is the detection of salient areas in natural video by using the new deep learning techniques. Salient patches in video frames are predicted first. Then the predicted visual fixation maps are built upon them. We design the deep architecture on the basis of CaffeNet implemented with Caffe toolkit. We show that changing the way of data selection for optimisation of network parameters, we can save computation cost up to @math times. We extend deep learning approaches for saliency prediction in still images with RGB values to specificity of video using the sensitivity of the human visual system to residual motion. Furthermore, we complete primary colour pixel values by contrast features proposed in classical visual attention prediction models. The experiments are conducted on two publicly available datasets. The first is IRCCYN video database containing @math videos with an overall amount of @math frames and eye fixations of @math subjects. The second one is HOLLYWOOD2 provided @math movie clips with the eye fixations of @math subjects. On IRCYYN dataset, the accuracy obtained is of @math . On HOLLYWOOD2 dataset, results in prediction of saliency of patches show the improvement up to @math with regard to RGB use only. The resulting accuracy of @math is obtained. The AUC metric in comparison of predicted saliency maps with visual fixation maps shows the increase up to @math on a sample of video clips from this dataset. Previous studies have shown that gaze direction of actors in a scene influences eye movements of passive observers during free-viewing (Castelhano, Wieth, & Henderson, 2007; Borji, Parks, & Itti, 2014). However, no computational model has been proposed to combine bottom-up saliency with actor’s head pose and gaze direction for predicting where observers look. Here, we first learn probability maps that predict fixations leaving head regions (gaze following fixations), as well as fixations on head regions (head fixations), both dependent on the actor’s head size and pose angle. We then learn a combination of gaze following, head region, and bottom-up saliency maps with a Markov chain composed of head region and non-head region states. This simple structure allows us to inspect the model and make comments about the nature of eye movements originating from heads as opposed to other regions. Here, we assume perfect knowledge of actor head pose direction (from an oracle). The combined model, which we call the Dynamic Weighting of Cues model (DWOC), explains observers’ fixations significantly better than each of the constituent components. Finally, in a fully automatic combined model, we replace the oracle head pose direction data with detections from a computer vision model of head pose. Using these (imperfect) automated detections, we again find that the combined model significantly outperforms its individual components. Our work extends the engineering and scientific applications of saliency models and helps better understand mechanisms of visual attention. This paper presents a novel fixation prediction and saliency modeling framework based on inter-image similarities and ensemble of Extreme Learning Machines (ELM). The proposed framework is inspired by two observations, (1) the contextual information of a scene along with low-level visual cues modulates attention, (2) the influence of scene memorability on eye movement patterns caused by the resemblance of a scene to a former visual experience. Motivated by such observations, we develop a framework that estimates the saliency of a given image using an ensemble of extreme learners, each trained on an image similar to the input image. That is, after retrieving a set of similar images for a given image, a saliency predictor is learnt from each of the images in the retrieved image set using an ELM, resulting in an ensemble. The saliency of the given image is then measured in terms of the mean of predicted saliency value by the ensembles members.