I have decided to release my UFO Bot Detection System that monitors Open Source Live Web-cameras found on the internet on Github here is the link! This can be a community project making the best eyes in the Skies!
I have decided to release my UFO Bot Detection System that monitors Open Source Live Web-cameras found on the internet on Github here is the link! This can be a community project making the best eyes in the Skies!
I have decided to release my UFO Bot Detection System that monitors Open Source Live Web-cameras found on the internet on Github here is the link! This can be a community project making the best eyes in the Skies! After the last month of working on this I feel confident enough to release this to the wider UFO Community. https://github.com/Eldest808/SkySentinelUFOBot/tree/main A UFO /UAP Detection Bot When an object is detected and tracked for 3+ seconds: Extracts real frames from the continuous buffer Includes 5 seconds BEFORE detection Includes 10 seconds AFTER detection Saves actual video (MP4, 30 FPS) Output structure: planes/ └── 20260321\_143022/ ├── 20260321\_143022.mp4 ← REAL video footage ├── 20260321\_143022.jpg ← Key frame └── 20260321\_143022\_metadata.json 📊 Monitoring The bot logs stats every 60 seconds: 📊 Stats: Frames=12500 | Objects=342 | Events=15 | Planes=12 | Birds=3 | UFOs=2 | Cameras monitored=4 | Skipped=1 Check logs at: logs/ufo\_bot.log ⚠️ Important Notes Bandwidth: 4 cameras at 30 FPS uses significant bandwidth CPU Usage: Monitor your CPU - reduce camera count if at 100% Camera Availability: Some cameras may be offline (bot auto-skips after 3 failures) Disk Space: Real video files are larger than time-lapse (expect \~5-20 MB per event) Layer What It Does False Positive Reduction Motion Detection 3-frame differencing, skips static frames 60-70% Light Detection Identifies luminous objects 20-30% Bird Filtering Size + behavior analysis 40-50% State Machine Requires 3+ confirmations 50-60% Trajectory Analysis Detects anomalous movement 30-40% Aircraft Filter Cross-references flight data 70-80% Combined Effect: 95-98% false positive reduction 🔍 How It Works Camera Frame → Motion Detect → YOLO → Bird Filter → Light Detect → State Machine → Trajectory Analysis → Aircraft Filter → UFO Candidate 📈 Expected Results Before: 50-100 recordings/day 95% false positives Hard to find genuine candidates After: 5-10 recordings/day Much higher quality Easy to identify genuine anomalies Dashboard UI Changes "+ Add New Link" button in top-right header (prominent placement as requested) Modal form for adding cameras (name, location, URL) CUSTOM FEEDS section displays added cameras Remove button (✕) on each custom camera card Backend Files Created custom\_cameras.py - Camera management system Save/load cameras from config/custom\_cameras.json Add, remove, update camera operations Integration with camera scraper format Files Modified UFOWatchDashboard.html Added "Add New Link" button to header Updated modal JavaScript to call API Enhanced add/remove functions with server sync proxy\_server.py Added /api/cameras endpoints (GET, POST, DELETE) Integrated custom camera manager camera\_scraper.py Loads custom cameras from config Merges with built-in cameras for scanner Documentation ADD\_CAMERA\_GUIDE.md - Complete user guide 🎨 How It Works User clicks "+ Add New Link" ↓ Fills in name, location, URL ↓ Dashboard calls POST /api/cameras ↓ Saved to config/custom\_cameras.json ↓ Camera appears in CUSTOM FEEDS section ↓ Bot restarts → Loads custom cameras ↓ Scanner monitors custom camera ↓ Detections recorded & notified submitted by /u/LegitimateKnee5537 [link] [comments]