Running AI inference on Raspberry Pi is possible, but stable operation needs careful pipeline design. Most failures come from bottlenecks in capture, preprocessing, or storage, not from the model itself.
1. End-to-end pipeline layout
I separate the workflow into independent stages:
- frame capture
- preprocessing and resizing
- inference
- event filtering
- persistence and upload
Each stage communicates through bounded queues so backpressure is visible and controlled.
2. Model selection constraints
Choose model based on latency budget, not leaderboard accuracy alone. For real-time edge use:
- prioritize quantized models
- measure end-to-end latency including preprocessing
- test false positives in your specific scene
A slightly less accurate model with predictable 30 ms inference can outperform a heavier model with unstable 150 ms spikes.
3. Resource budgeting on Pi
Profile these resources continuously:
- CPU usage by stage
- memory use and queue depth
- thermal status and throttling flags
- storage write rate
When queue depth grows over time, your pipeline is overloaded even if instantaneous CPU looks acceptable.
4. Event logic and noise suppression
Raw detections are noisy. Apply event rules before triggering actions:
- confidence threshold
- minimum consecutive detections
- object persistence window
- cooldown before duplicate alerts
This converts jittery frame-level predictions into useful operational events.
5. Data retention strategy
Edge cameras can produce large storage pressure quickly. Keep policy explicit:
- store full frames only around events
- keep low-res previews for context
- prune old media by TTL and quota
If retention is undefined, disk exhaustion becomes inevitable.
6. Reliability features
Add operational safety:
- watchdog for stalled capture
- service supervision via
systemd - startup self-check for camera and model files
- graceful degradation mode (capture only, no inference)
A degraded mode is better than total outage when hardware is constrained.
7. Security and privacy
Camera systems handle sensitive data. Baseline controls:
- encrypt remote transport
- restrict dashboard access
- rotate credentials
- audit who can access stored media
Avoid exposing camera feeds directly to the public internet.
8. Validation checklist
Before deployment:
- test day/night lighting transitions
- simulate network outage and recovery
- verify behavior during thermal throttling
- run 48-hour stability soak test
Short demos do not reveal long-run pipeline drift.
Final note
Edge AI on Raspberry Pi succeeds when the full system is engineered, not only the model. Queue discipline, event filtering, and recovery behavior matter as much as inference speed.