Low-light Environment Neural Surveillance
This addresses the problem of proactive surveillance for law enforcement and public safety in low-light conditions, though it is incremental as it builds on existing action-recognition and IoT methods.
The paper tackles real-time crime detection in low-light environments by developing an end-to-end system that processes video feeds to identify shootings, assaults, and thefts, achieving 71.5% accuracy at 20 FPS.
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.