MEDUSA Multi Environment Deployable Universal Software Application

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使用的闭路电视摄像机网络来实现。然而，操作员在解读闭路电视图像方面的表现参差不齐，因为他们试图基本上检测到一种非常罕见的威胁事件。此外，目前用于检测可能的异常行为的自动化系统已被发现取得了不同程度的成功。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，该系统将结合人和机器的因素。这项提案使用选定的闭路电视监控录像，其中描绘了持有隐蔽枪支的人和其他控制人员，将确定闭路电视操作员在从此类闭路电视图像中识别潜在持枪个人时，实际关注的是哪些公开和隐蔽的线索(本质上是有意识的和潜意识的线索)。同时，机器学习方法将为这些人建立机器识别的线索。然后，单独的人类和机器线索将被组合起来，形成一种新的机器学习方法，该方法将得到充分的测试。该系统将能够学习并对当地枪支犯罪因素作出反应，这将有助于其有用性和部署能力。

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.