Challenge
Deliver low-latency, privacy-aware people and object analytics across a fleet of Raspberry Pi edge devices, with enriched demographic insights and centralized observability, while ensuring maintainability and continuous deployment.
Objectives
- Count people and multiple object classes accurately.
- Infer age and gender (estimation, non-identifying).
- Stream statistical metrics to a time‑series backend.
- Visualize trends and performance in real time.
- Optimize models for constrained edge hardware + attached TPUs.
- Uniform remote configuration and automated updates.
- Extend metrics beyond raw counts (e.g., attention, distance, share ratios).
Solution Overview
- Computer Vision Pipeline: Multi-stage inference (detection → attribute estimation → tracking) tuned for edge latency.
- Demographic & Movement Layer: Lightweight age/gender estimators;
- Metrics & Telemetry: Aggregated event → transformation → batching → write to InfluxDB (Flux queries powering Grafana dashboards).
- Advanced Metrics: Attention (dwell/time-in-frame), distance (camera geometric calibration), share stats (class proportion analysis).
- Fleet Management: Configuration manager per device (versioned profiles, remote overrides).
- CI/CD: Pipeline building, artifact packaging, staged rollout to Raspberry Pi devices (canary then full deploy).
- Resilience: Health checks, auto-restart on inference stalls, metric backpressure handling.
Technology & Methods
- Edge Hardware: Raspberry Pi + attached TPUs.
- ML Techniques: Object detection, re-identification / tracking, lightweight attribute classification, model compression.
- Data Layer: InfluxDB (time-series), Flux QL dashboards in Grafana.
- Deployment: Automated push updates, configuration synchronization.
- Observability: Custom stats emitter + Grafana panels (counts, dwell, performance, device health).
Key Innovations
- Unified pipeline combining detection, demographic estimation, and tracking under tight resource constraints.
- Dynamic configuration manager enabling per-location tuning without code changes.
- Extended behavioral metrics (attention & distance) enriching raw count analytics.
Outcomes
- Reliable real-time counts and movement insights across all deployed edge units.
- Reduced inference latency via model optimization for TPUs.
- Faster rollout cycles and consistent configuration through CI/CD and management layer.
- Actionable dashboards for operational and analytical decision-making.
Impact
Enhanced situational awareness, improved utilization insights, and scalable foundation for future analytic modules.
Future Extensions
- Adaptive learning (periodic on-device fine-tuning).
- Privacy-preserving synthetic aggregation.
- Predictive occupancy forecasting.
- Anomaly detection (unexpected movement patterns).
Summary
A production-grade, telemetry-rich edge analytics system delivering multi-dimensional people/object insights with optimized ML performance and robust operational tooling.