IDLE
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🏃 Runner detected
POSE DEBUG
Detection Mode
Amplitude Threshold
Trigger level -15 dB
Audio level (dB) that triggers a capture. Lower = more sensitive.
Audio Input
Input gain 1.0x
Cooldown period 5s
Minimum time between triggers to prevent duplicate captures
Clip Duration
Pre-roll (before hit) 10s
Seconds of video kept BEFORE the detected hit
Post-roll (after hit) 20s
Seconds of video recorded AFTER the detected hit
Camera Settings
Resolution
Frame rate
Camera
Pose Detection
Enable pose detection
Uses MoveNet to detect human poses and track runners on the field. Increases CPU/GPU usage.
Model
Variant
Lightning recommended for mobile. Thunder for tripod/stationary setups.
Overlay
Show skeleton
Show keypoint labels
Performance
Detection FPS 10 FPS
Higher FPS = smoother tracking but more CPU load
Base Runner Tracking
Auto-stop on base arrival
Automatically end clip recording when a runner reaches a base zone
Tap to define where each base appears in the camera view
Manual Controls
Simulate a bat crack detection for testing the capture pipeline
Audio Spectrogram
Pose Data
Enable pose detection to see data
Event Log
Settings
System
Heap: --/-- MB
Keyboard Shortcuts
Space — Start/Stop listening
T — Manual trigger
Esc — Close panel / preview
Captured Clips 0
Future: Auto-Export
A future version will auto-export clips to cloud storage, Google Drive, or a custom API endpoint. For now, use the Download buttons to save clips locally.
by Mathew Sanford

ML Audio Capture

Real-time audio event detection with automatic video capture. Uses TensorFlow.js for ML classification, MoveNet for pose estimation, and a ring-buffer recording pipeline.

📹
Camera
🎙
Microphone
🧠
ML Model

Best on Chrome Android in landscape orientation.

📱

Rotate to Landscape

Baseball fields are wider than tall — landscape mode gives the best view and more accurate pose tracking.