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; 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.

人类的观察者移动着眼睛，以将注意力引向视觉场景的重要方面。有一些称为显着图的模型；他们预测看一看场景时的眼睛会转移到哪里。目前，这些模型并不涉及视频输入，也没有预测观察者的任务将如何影响他们的外观。换句话说，没有针对现实生活中的情况的模型，观察者具有特定的任务。我们正在为此问题提出一种新方法。我们可以访问来自城市监视中使用的相机的视频信息，以及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）在英国城市街道上的事件的视频录制，并将其显示在模拟的控制室中，向熟悉该城镇的操作员。我们将监视他们在视频屏幕库上的位置，以及当他们决定事件异常和/或需要某种形式的干预措施时，例如打电话警察。我们将使用注视记录来教授计算机系统，以区分正常事件和异常事件。通过这种方式，我们将能够通过观察他们的眼睛固定行为，实现现实（和困难）的任务以及一组真实的视频序列来学习对人类进行此类监视的重要内容。该项目很重要，原因有四个。首先，这将是开发人类注意力/眼睛运动模型的首次尝试，该模型将基于现实的视频输入和实际任务而坚定地基于。其次，这将是计算机系统第一次能够以这种方式从人类行为中学习。第三，随着电视显示器数量的增加，我们将了解训练有素的观察者应对苛刻任务的能力。最后，我们将开发一个自动化系统，该系统将能够分析任何城市CCTV摄像机的输入，以便提醒操作员查看该视频流 - 目前，任何人都没有观察到大多数CCTV视频流，因为对于人类观察者的数量来说，相机太多。因此，自动警报系统被大大增加了，该项目构成了约会的最佳尝试。