Attention Tracking using Shared Attention Modeling and Attentional Push

我们的研究项目关注的是注意力追踪--确定人们的注意力集中在哪里和什么地方。特别是，我们感兴趣的是测量和预测人们观看静态照片，计算机显示器，以及观看视频或电影时的视觉注意力。现有的基于眼动跟踪的注意力跟踪方法工作得很好，但可以改进。我们的研究小组在最近的工作中研究了一个改进的领域，该领域涉及观众任务的推理及其与注意力分配的关系。了解观众正在做什么任务，如阅读、计数或搜索，对于微调观众的突出概念至关重要。然而，即使这是不够的-一个随机选择的人仍然会做一个更好的工作，预测另一个人会看，甚至比目前最好的注意力跟踪算法。当前方法的缺点之一是，在大多数情况下，它们集中于分析图像的区域以获得吸引注意力的能力。正在提出的研究计划超越了这些方法，而不是只计算图像区域的“拉”注意力的力量，我们还考虑了图像的其他区域的“推”注意力到该区域的问题。图像中的对象或特征被分析为观看者注意力的主动操纵器。例如，标志上的人造箭头有目的地引导观众去看哪里，而照片中的会聚线可以将观众的注意力吸引到图像中的预期主题。已知图像中的面部在将观看者的注意力引导到面部注视的方向上方面特别有效。我们使用术语“注意力推动”来指图像区域的力量，以指导和操纵观众的注意力分配。我们的研究将开发计算注意力推送的技术，然后将其与标准的基于图像显着性的注意力建模算法相结合，以提高预测观众将注视的能力。在开发计算注意力推动的技术时，我们将使用从人类和机器人的“共享注意力”研究中获得的知识。共享注意力是多个“演员”将注意力集中在同一事物上的过程。我们的共享注意力方法是识别图像中的演员，然后可以分析他们的注意力推动，潜在地指导和操纵观众的注意力分配。所提出的工作有许多实际应用超出理解图像观众的注意。我们可以使用开发的方法来确定在许多人聚集的区域的监视中共享注意力的集体焦点，这可以用于分析群体活动的视频，例如体育赛事，并预测这些视频的观众的注意力。

Our research program is concerned with attention tracking - determining where, and to what, people are paying attention. In particular, we are interested in measuring and predicting the visual attention of people viewing static photographs, computer displays, but also while watching videos or cinematic movies. Existing attention tracking methods based on eye tracking work well, but can be improved. One area of improvement was examined in recent work by our research group which concerned the inference of viewer task and its relation to the allocation of attention. Knowing what task a viewer is doing, such as reading, counting, or searching, is crucial to fine-tuning the notion of what is salient for that viewer. However, even this is not enough - a single randomly selected human will still do a better job of predicting where another human will look than even the best current attention tracking algorithms. One of the shortcomings of the current approaches is that, for the most part, they concentrate on analyzing regions of the image for their power to attract attention. The research program being proposed goes beyond these approaches and instead of only computing the power of an image region to 'pull' attention to it, we also consider the strength with which other regions of the image 'push' attention to the region in question. Objects or features in the image are analyzed as active manipulators of the viewers' attention. For example, man-made arrows on signs purposefully direct the viewer where to look, while converging lines in photographs can draw the viewers' attention to the intended subject in the image. Faces in images are known to be particularly effective in directing viewers attention in the direction of the face's gaze. We use the term 'Attentional Push' to refer to the power of image regions to direct and manipulate the attention allocation of the viewer. Our research will develop techniques for computing Attentional Push, which can then be integrated with standard image salience-based attention modeling algorithms to improve the ability to predict where viewers will fixate. In developing our techniques for computing Attentional Push, we will use knowledge gained from studies of 'Shared Attention' in humans and robots. Shared Attention is the process by which multiple 'actors' come to focus their attention on the same things. Our approach to Shared Attention is to identify the actors in the image, which can then be analyzed for their Attentional Push, potentially directing and manipulating the attention allocation of the viewer. The proposed work has many practical applications beyond understanding the attention of image viewers. We can use the methods developed to determine the collective focus of shared attention in surveillance of areas where many people gather, which could be used to analyze videos of group activities, such as sporting events, and predict the attention of viewers of these videos.