Low-light Environment Neural Surveillance

TL;DR

This paper presents a real-time low-light surveillance system using neural networks and IoT infrastructure to detect crimes and alert authorities, with a new dataset and mobile app integration.

Contribution

It introduces an end-to-end low-light crime detection system with real-time processing, a new dataset LENS-4, and integrated mobile alert applications.

Findings

Achieves 71.5% accuracy at 20 FPS.

Develops the LENS-4 dataset for low-light action recognition.

Implements a scalable IoT infrastructure for real-time alerts.

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.