BrandonJoffe/home_surveillance
Home Surveillance
This project is a computer vision-based security platform designed for real-time property surveillance and automated threat detection. It integrates live video stream management with neural network processing to monitor, track, and identify individuals within a monitored environment.
The system distinguishes itself through local identity management, which allows users to train classifiers on specific individuals and store facial embeddings directly on the local system. By combining this biometric analysis with event-driven logic, the platform can differentiate between known persons and unknown intruders, triggering automated alerts or physical hardware responses when unauthorized activity is detected.
The platform provides a centralized web-based dashboard that facilitates the management of multiple network cameras and the delivery of live video feeds. It utilizes persistent communication channels to ensure low-latency updates and notifications, supporting continuous monitoring of workspaces or residential properties.
Features
- Security Monitoring - Manages and views live video feeds from network cameras to keep a watchful eye on your property or workspace.
- Real-Time Object Detection - Processes live video frames through deep learning models to identify and track human faces in real time.
- Facial Recognition - Uses neural networks to identify known individuals and detect unknown intruders within live video streams for enhanced access control.
- Live Video Stream Monitoring - Transmits real-time video from network cameras to a web-based dashboard for remote surveillance and monitoring of property or workspaces.
- Security Alert Triggers - Configures motion and facial detection triggers to send instant notifications or activate physical alarms when potential threats are identified.
- Computer Vision Systems - Provides a surveillance platform that uses neural networks to detect and identify faces in live camera feeds for automated security alerts.
- Face Detection - Processes live camera feeds using neural networks to identify and track individuals in real time for activity logging.
- Face Recognition Training - Trains classifiers on local image sets to identify specific individuals and update the database of known people for accurate access control.
- Facial Recognition Classifiers - Trains local classifiers to recognize specific individuals and distinguish between known persons and unknown intruders in video.
- Local-First Databases - Stores facial embeddings and training sets locally to enable offline classification and recognition of known individuals.
- Video Streaming Pipelines - Transmits raw camera data through a series of buffers to ensure low-latency delivery to the web-based monitoring dashboard.
- Event-Driven Automations - Triggers physical alarms and external notifications by monitoring state changes from motion sensors and facial recognition logic.
- Camera Management Interfaces - Provides a web interface for streaming multiple live video feeds and managing security configurations for remote property monitoring.
- Camera Management Platforms - Centralizes the control and streaming of multiple security cameras into a single web dashboard for easier remote surveillance.
- Intruder Detection Systems - Analyzes live camera feeds using neural networks to distinguish between known individuals and unknown persons to detect potential security breaches.
- Real-Time Communication - Maintains persistent connections between the surveillance server and the browser interface to push live video and alert updates.
