标题:弱光环境神经监视

摘要】我们设计并实现了一个端到端系统,用于在光线昏暗的环境中实时检测犯罪。与闭路电视不同,闭路电视具有反应性,低光环境神经监视可提供实时犯罪警报。该系统使用由光流网络,空间和时间网络以及支持向量机实时处理的微光视频源,以识别枪击,袭击和盗窃。我们创建了一个微弱的动作识别数据集LENS-4,该数据集将公开提供。通过Amazon Web Services建立的IoT基础架构会解释来自托管摄像机的本地板的消息以进行动作识别,并解析云中的结果以中继消息。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,可让地方当局接收通知并查看犯罪现场的视频。公民拥有一个公共应用程序,该应用程序可使执法部门根据用户的接近程度来推送犯罪警报。

Title: Low-light Environment Neural Surveillance

[abstract]  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.

【作者】Michael Potter (1), Henry Gridley (1), Noah Lichtenstein (1), Kevin Hines (1), John Nguyen (1), Jacob Walsh (1) ((1) Northeastern University)

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