MEDUSA Multi Environment Deployable Universal Software Application

MEDUSA 多环境可部署通用软件应用程序

• 批准号: EP/D078830/1
• 负责人: Alastair Gale
• 金额: $ 50.8万
• 依托单位: Loughborough University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD078830%2F1
• 关键词: MEDUSA; Multi; Environment; Deployable; Universal

A key factor in reducing potential gun crime is to detect someone carrying a gun before they can commit a criminal act. This detection can be achieved by the existing, and widespread, CCTV camera network in the UK. However, the performance of operators in interpreting CCTV imagery is variable as they are trying to detect essentially a very rare threat event. Additionally, current automated systems for detecting possible anomalous behaviour have been found to have varying success. We propose the development of a new machine learning system for the detection of individuals carrying guns which will combine both human and machine-based factors. Using selected CCTV footage which depicts people carrying concealed guns, and other control individuals, the proposal will establish what overt and covert cues (essentially conscious and subconscious cues) experienced CCTV operators actually attend to when identifying potential gun-carrying individuals from such CCTV imagery. In parallel, a machine learning approach will establish the machine recognised cues for such individuals. The separate human and machine cues will then be combined to form a new machine learning approach which will be fully tested. The system will be capable of learning and reacting to local gun crime factors which will aid its usefulness and deployment capability.

减少潜在枪支犯罪的一个关键因素是在某人实施犯罪行为之前发现他们携带枪支。这种检测可以通过英国现有的、广泛的闭路电视摄像机网络来实现。然而，操作员在解释CCTV图像方面的表现是可变的，因为他们试图检测基本上是非常罕见的威胁事件。此外，已经发现用于检测可能的异常行为的当前自动化系统具有不同的成功。我们建议开发一种新的机器学习系统，用于检测携带枪支的个人，该系统将联合收割机结合人类和机器因素。使用选定的闭路电视镜头，描绘携带隐藏枪支的人，和其他控制个人，该提案将建立什么样的公开和隐蔽的线索（基本上是有意识和潜意识的线索）经验丰富的闭路电视操作员实际上参加时，确定潜在的枪支携带个人从这样的闭路电视图像。同时，机器学习方法将为这些人建立机器识别的线索。然后，将单独的人类和机器线索结合起来，形成一种新的机器学习方法，并对其进行全面测试。该系统将能够学习和应对当地的枪支犯罪因素，这将有助于其实用性和部署能力。

项目成果

The role of emotion recognition from non-verbal behaviour in detection of concealed firearm carrying

非语言行为的情绪识别在检测隐藏携带枪支中的作用

• 发表时间: 2009
• 作者: A Blechko

Exploring essential skills of CCTV operators: the role of sensitivity to nonverbal cues

探索闭路电视操作员的基本技能：对非语言线索的敏感性的作用

• 发表时间: 2008
• 作者: A Blechko

Potential applications of affective computing in the surveillance work of CCTV operators.

情感计算在闭路电视操作员监控工作中的潜在应用。

• 作者: Anastasia Blechko (Author)

Automation of the CCTV-mediated detection of individuals illegally carrying firearms: combining psychological and technological approaches

通过闭路电视自动检测非法携带枪支的个人：结合心理和技术方法

• DOI: 10.1117/12.819998
• 发表时间: 2009
• 作者: Darker I

Lights, camera, action and arrest.

灯光、摄像机、动作和逮捕。

• 作者: Iain Darker (Author)