论文标题
弱光环境神经监测
Low-light Environment Neural Surveillance
论文作者
论文摘要
我们在弱光环境中设计和实施一个用于实时犯罪检测的端到端系统。与闭路电视的作用不同，低光环境神经监视可提供实时犯罪警报。该系统使用光流网络，空间和时间网络以及支持向量机的实时处理的低光视频提要来识别枪击，攻击和盗窃。我们创建一个低光的动作识别数据集Lens-4，将公开使用。通过Amazon Web服务设置的IoT基础架构解释了本地板上托管相机以进行操作识别的消息，并将结果解析在云中以中继消息。该系统在20 fps时达到71.5％的精度。用户界面是一个移动应用程序，允许地方当局接收通知并观看犯罪现场的视频。公民有一个公共应用程序，该应用程序使执法部门能够根据用户邻近性来推动犯罪警报。
We design and implement an end-to-end system for real-time crime detection in low-light environments. Unlike Closed-Circuit Television, which performs reactively, the Low-Light Environment Neural Surveillance provides real time crime alerts. The system uses a low-light video feed processed in real-time by an optical-flow network, spatial and temporal networks, and a Support Vector Machine to identify shootings, assaults, and thefts. We create a low-light action-recognition dataset, LENS-4, which will be publicly available. An IoT infrastructure set up via Amazon Web Services interprets messages from the local board hosting the camera for action recognition and parses the results in the cloud to relay messages. The system achieves 71.5% accuracy at 20 FPS. The user interface is a mobile app which allows local authorities to receive notifications and to view a video of the crime scene. Citizens have a public app which enables law enforcement to push crime alerts based on user proximity.
