Skip to main content

🩻 Bee pose generation

🎯 Purpose​

Generate detailed morphometric models and pose estimation for individual bees to enable advanced behavioral analysis and health monitoring.

🎭 User Story​

  • As a researcher or advanced beekeeper
  • I want to analyze detailed bee body positions and movements
  • So that I can detect abnormal behaviors, health issues, and understand complex bee interactions at a granular level

🚀 Key Benefits​

  • Health diagnostics: Detect abnormal postures indicating disease or injury
  • Behavioral analysis: Understand complex bee movements and communication
  • Research advancement: Contribute to scientific understanding of bee biomechanics
  • Quality assessment: Identify bee morphological variations and subspecies characteristics

🔧 Technical Overview​

Implements deep learning pose estimation models to detect and track bee body parts (head, thorax, abdomen, wings, legs) in video frames. Builds on existing computer vision infrastructure to provide detailed morphometric analysis similar to human pose estimation systems.

📋 Acceptance Criteria​

  • Detects major bee body parts (head, thorax, abdomen, wings) with >75% accuracy
  • Tracks leg positions and wing orientations
  • Generates pose keypoints compatible with research standards
  • Processes multiple bees simultaneously in frame
  • Exports pose data in standard research formats (JSON, CSV)
  • Maintains processing speed >10 FPS for pose analysis

🚫 Out of Scope​

  • Microscopic detail analysis (cellular level)
  • 3D pose reconstruction from single camera
  • Real-time pose tracking for all bees (subset processing only)
  • Automated health diagnosis (pose data only)

🏗️ Implementation Approach​

  • Foundation: Extend existing beepose models from Gratheon/models-beepose
  • Architecture: Custom CNN similar to DeepBees morphometric approach
  • Training data: Leverage LabelBee platform annotated datasets
  • Integration: Build on existing bee detection and tracking pipeline
  • Output: Standardized keypoint format for research compatibility

📊 Success Metrics​

  • Pose keypoint accuracy >75% on test dataset
  • Processing capability for 3+ bees simultaneously
  • Model training convergence within reasonable compute budget
  • Research community adoption of output format
  • Integration success with existing tracking systems

🔗 Related Features​

📚 Resources & References​

💬 Notes​

High research value but computationally intensive. Should be developed as optional add-on to core tracking features. Potential collaboration opportunity with academic institutions.