Cognitive Systems Foresight: Human Attention and Machine Learning

认知系统前瞻：人类注意力和机器学习

• 批准号: EP/E010164/1
• 负责人: David Hogg
• 金额: $ 42.57万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE010164%2F1
• 关键词: Cognitive; Systems; Foresight; Human; Attention

Human observers move their eyes in order to direct their attention to important aspects of a visual scene. There are models called salience maps; they predict where the eyes will move to when looking at a scene. At present, these models do not deal with video input, nor do they predict how an observer's task will affect where they look. In other words, there are no models for real-life viewing situations, where an observer has a specific task.We are proposing a new approach to this problem. We have access to video information from cameras used in urban surveillance, and to the operators whose job it is to spot abnormal behaviour in such video inputs. We shall obtain (previously unseen) video recordings of events in UK urban streets, and display them in a simulated control room to operators familiar with the town in question. We shall monitor where they look on the bank of video screens, and also when they decide that an event is abnormal and/or requires some form of intervention, e.g. calling the police. We shall use the record of eye fixations to teach a computer system to distinguish between normal and abnormal events. In this way, we shall be able to learn what is important for humans to do such surveillance by observing their eye fixation behaviour, for a realistic (and difficult) task and set of real-life video sequences. The project is important for four reasons. First, this will be the first attempt to develop a model of human attention/eye movements which will be firmly based on realistic video input and a real task. Second, this will be the first time that a computer system is able to learn from human behaviour in this way. Third, we will learn much about the ability of trained observers to cope with a demanding task as the number of TV monitors increases. Finally, we will develop an automated system which will be able to analyse the input from any urban CCTV camera in order to alert operators to look at that video stream - at present, most CCTV video streams are not observed by anyone since there are too many cameras for the number of human observers. Therefore, an automated alerting system is greatly neeeded and this project constitutes the best attempt to date to produce one.

人类观察者移动他们的眼睛，以便将他们的注意力引导到视觉场景的重要方面。有一种模型叫做显着图，它们预测眼睛在观看场景时会移动到哪里。目前，这些模型不处理视频输入，也不能预测观察者的任务将如何影响他们的视线。换句话说，在现实生活中，观察者有一个特定的任务，没有模型。我们提出了一个新的方法来解决这个问题。我们可以从城市监控中使用的摄像机获得视频信息，并接触到负责在此类视频输入中发现异常行为的操作员。我们将获得（以前看不见的）英国城市街道事件的视频记录，并在模拟控制室中向熟悉有关城镇的操作员展示。我们将监控他们在视频屏幕上的位置，以及当他们认为事件异常和/或需要某种形式的干预时，例如报警。我们将使用眼睛注视的记录来教计算机系统区分正常和异常事件。通过这种方式，我们将能够通过观察人类的眼睛注视行为来了解人类进行这种监视的重要性，以完成现实（和困难）的任务和一组真实的视频序列。该项目之所以重要，有四个原因。首先，这将是开发人类注意力/眼球运动模型的第一次尝试，该模型将牢固地基于逼真的视频输入和真实的任务。其次，这将是计算机系统首次能够以这种方式从人类行为中学习。第三，随着电视监视器数量的增加，我们将更多地了解受过训练的观察员科普艰巨任务的能力。最后，我们将开发一个自动化系统，该系统将能够分析来自任何城市闭路电视摄像机的输入，以提醒操作员查看该视频流-目前，大多数闭路电视视频流没有被任何人观察到，因为对于人类观察者来说，摄像机太多了。因此，一个自动报警系统是非常必要的，这个项目是迄今为止最好的尝试，以生产一个。

项目成果

Human Factors Security and Safety

人为因素安全保障

• 发表时间: 2009
• 作者: Howard, C J

Suspiciousness perception in dynamic scenes: a comparison of CCTV operators and novices.

• DOI: 10.3389/fnhum.2013.00441
• 发表时间: 2013
• 期刊: Frontiers in human neuroscience
• 影响因子: 2.9
• 作者: Howard CJ;Troscianko T;Gilchrist ID;Behera A;Hogg DC
• 通讯作者: Hogg DC