About this Application
Overview
SecureMotion is a lightweight, privacy-focused local security hub designed to execute directly within a modern web browser.
Rather than forwarding camera feeds to remote cloud instances, this hub manages all frames inside the processing thread of the
host browser. This architecture avoids sending raw imagery outside the computer, offering an extra layer of privacy.
By utilizing local hardware interfaces, SecureMotion analyzes, flags, and records security activities locally.
How to Use
- Define Storage Folder: Click Select Folder. Assign a storage path on the host computer. Files will be recorded and retrieved directly from this location.
- Start Video Feed: Choose the Start Camera action. Give the browser permission to access the local webcam interface.
- Tune Sensitivity Threshold: Slide the threshold controller to align motion actions. Lower bounds limit detection triggers to wide-scale transitions, whereas high bounds capture subtle changes.
- Arm the Hub: Activate the Arm System checkbox. The algorithm will begin saving a 3-second security video segment when motion is identified.
- System Sound Alerts: Toggle the Sound: ON/OFF trigger to enable synthetic alert tones upon detection.
- Review Footage: Use the direct playback controls on cards loaded inside the Recordings feed. Press Delete to clear files from the local storage folder.
- Reset System: Use the Reset Simulation option to clear and restore the application states.
Technical Details
- Secure Media Capture: Leverages the navigator.mediaDevices.getUserMedia media stream layer.
- Frame Differencing: Draws active webcam arrays to an isolated virtual canvas. By comparing pixel values (RGB channels) across subsequent frames, changes in localized pixel structures are calculated to determine motion coordinates.
- Direct Stream Encoding: Employs the MediaRecorder API (using VP8/WebM fallback encoding schemes) to bundle frame segments locally.
- Direct Local IO: Implements the FileSystem Access API to directly write records onto host folder handles, avoiding arbitrary user download dialogues.
Future Directions
The roadmap for this system focuses on integrating lightweight machine learning models (such as TensorFlow.js) to process edge-based classification profiles. This step aims to differentiate between humans, pets, and wind-blown elements directly in-browser. Additionally, updates will target support for synchronized multi-channel streams and WebRTC integrations to securely view camera streams from other local devices.