IoTwarehouseautomation

Build a Micro Video Analytics Pipeline for Warehouses with Pi + AI HAT+ 2

UUnknown

2026-02-16

9 min read

Deploy a warehouse-scale video analytics pipeline in 2026 using clustered Raspberry Pi 5 + AI HAT+ 2, MQTT, and Kafka for people counting, inventory detection and anomaly alerts.

Hook: Why warehouse teams are stretched — and how Pi + AI HAT+ 2 fixes it

Your warehouse is a fast-moving, error-prone system: people flow, misplaced pallets, and blind spots cost time and money. Manual counts and cloud-only video processing are either slow or expensive. In 2026, operations teams want real-time insights at edge scale — low latency, privacy-friendly, and easy to iterate on. This tutorial shows how to build a production-ready, horizontally scalable video analytics pipeline for warehouses using clustered Raspberry Pi 5 + AI HAT+ 2 devices with MQTT message queues, and a simple central streaming layer for analytics, alerts, and integrations.

What you'll get in this guide

An architecture blueprint for an edge-first video analytics pipeline
Step-by-step deployment patterns for clustered Raspberry Pis with AI HAT+ 2
Code and config snippets: MQTT, Mosquitto gateway, lightweight inference, and alerting
Scaling, security and operational best-practices for 2026 warehouse automation

Why this matters now (2026 trends)

Edge computing and integrated automation are the dominant trends shaping warehouse tech in 2026. Industry playbooks now recommend combining workforce optimization with edge AI to reduce latency and operational risk. The Raspberry Pi 5 + AI HAT+ 2 (released late 2025) opened affordable on-device acceleration, letting teams push inference to the edge rather than paying cloud egress and GPU time for continuous video streams. As DC Velocity and other supply-chain briefings emphasized this year, the practical wins come from tightly integrated, data-driven automation — not isolated point solutions.

"Automation strategies are evolving beyond standalone systems to more integrated, data-driven approaches that balance technology with labor realities." — Designing Tomorrow's Warehouse (2026)

System overview — the architecture

The pipeline we’ll build favors edge-first processing, with a lightweight central plane for aggregation and long-term analytics. Components:

Edge nodes: Raspberry Pi 5 + AI HAT+ 2 + camera per zone. Run local inference for people counting, item detection and micro-anomaly checks.
Local gateway (edge broker): Mosquitto MQTT on a nearby microserver or leader Pi to aggregate telemetry and ensure resilient delivery. Consider autoscaling and connector patterns from auto-sharding blueprints: Mongoose.Cloud Auto-Sharding.
Central streaming/ingest: Kafka or managed streaming for warehouse-wide event processing and enrichment.
Alerting & integrations: Prometheus/Grafana for metrics, Alertmanager for thresholds, and webhooks/Slack/incident systems for real-time alerts. Operational tradeoffs for hybrid cloud ops are covered in Distributed File Systems for Hybrid Cloud.

Data flow (high level)

Camera stream → on-device inference (detection + tracking)
Edge node publishes events (counts, detections, anomalies) to MQTT
Edge gateway forwards MQTT to Kafka (or cloud) for aggregation and storage
Central processors run enrichment, inventory reconciliation, SLA checks
Alerts fired via Alertmanager/webhooks when rules trigger

Hardware and software prerequisites

Raspberry Pi 5 (recommended) × N
AI HAT+ 2 module for each Pi (on-device NPU referenced in late-2025 announcements)
USB/CSI cameras for each Pi
Network: VLANs for security, PoE switch recommended
Edge gateway: small server or Pi leader for Mosquitto
Central broker: Kafka cluster (or cloud-managed) and Prometheus/Grafana

Step 1 — Prepare the Raspberry Pi node image

Build a reproducible OS image with the runtime, drivers and a container runtime. I recommend Alpine or Raspberry Pi OS (64-bit) and Docker. Use a config management tool (Ansible/ignition) for fleet consistency. For developer tooling and CLI workflows, see a recent review of developer CLIs and UX: Oracles.Cloud CLI vs Competitors.

Essential packages

Docker (for containerized models and utilities)
MQTT client libraries (paho-mqtt for Python)
ONNX Runtime / PyTorch Mobile or the vendor NPU SDK that AI HAT+ 2 provides
Motion/UV4L if you need local RTSP access

Sample systemd service to start the inference container

[Unit]
Description=Edge Inference Service
After=docker.service

[Service]
Restart=always
ExecStart=/usr/bin/docker run --rm --gpus all --device=/dev/video0 --name edge-infer \
  -e MQTT_BROKER=192.168.1.10:1883 \
  registry/local/warehouse-infer:latest

[Install]
WantedBy=multi-user.target

Step 2 — Local inference: people counting & inventory detection

On-device inference runs two primary tasks: people counting and inventory detection. Keep models small — goal is real-time, not high-res segmentation.

Model suggestions

People: lightweight object detector (YOLOv8n/RTMDet nano) or a Tiny-YOLO variant compiled for the AI HAT+ 2 NPU.
Inventory: class-based detectors for pallets, boxes, and key SKU shapes or barcode/QR scanners for labeled items.
Tracking: SORT or ByteTrack for per-frame association and robust counting.

Convert models to ONNX or the HAT vendor’s optimized format and load on startup. Use batch size 1, and apply confidence thresholds. Each detection package should include timestamp, bbox, class, confidence, and track_id.

Publish compact events over MQTT (example)

import time
import json
import paho.mqtt.client as mqtt

mqttc = mqtt.Client()
mqttc.connect('EDGE_GATEWAY_IP', 1883)

def publish_detection(zone, data):
    topic = f"warehouse/{zone}/events"
    mqttc.publish(topic, json.dumps(data), qos=1)

# after inference
event = {
  "ts": int(time.time()*1000),
  "type": "detection",
  "zone": "packing-A",
  "counts": {"people": 4},
  "detections": [
    {"id": 12, "cls": "person", "conf": 0.86}
  ]
}
publish_detection('packing-A', event)

Step 3 — Edge gateway and message durability

Use an edge gateway running Mosquitto to act as a resilient local MQTT broker. This keeps telemetry local if connectivity drops and reduces re-connections from many devices to the central cluster.

Lightweight Docker Compose for Mosquitto

version: '3.8'
services:
  mosquitto:
    image: eclipse-mosquitto:latest
    volumes:
      - ./mosquitto.conf:/mosquitto/config/mosquitto.conf
      - ./data:/mosquitto/data
    ports:
      - 1883:1883
      - 9001:9001 # websocket if needed

Configure Mosquitto persistence and authenticated clients. Then use a connector (Kafka Connect MQTT source or a tiny bridge) to forward messages to Kafka for long-term processing and analytics.

Step 4 — Central ingestion, enrichment and anomaly detection

Central processors consume the stream and run multi-zone correlation, inventory reconciliation with WMS, and anomaly detection. Anomalies to watch for:

Sudden people count spike/drop in a zone
Inventory detection mismatch vs expected stock levels
Repeated camera obstructions (camera static detection)

Simple anomaly rule (example)

if people_count > expected_peak + 20%:
    fire_alert(zone, 'crowd spike')

if inventory_detections_missing > threshold:
    create_work_order('manual_check', zone)

For more adaptive detection, use a rolling baseline (exponential moving average) per zone and a small anomaly model (isolation forest or a 1D CNN) that consumes time-series counts and detection scores. For examples of anomaly and incident simulation and response, security runbooks are a useful reference: Simulating an Autonomous Agent Compromise.

Alerting, dashboards and ops

Instrument your edge and central components with Prometheus exporters. Track these key metrics:

Per-node inference latency (95th percentile)
MQTT publish success / loss
People counts per zone and per minute rates
Inventory detection confidence by SKU

Use Grafana dashboards to visualize flows, and configure Alertmanager rules. Example alert: if a zone’s people count drops to zero during operating hours and the camera is online, trigger a camera-health check before creating a work ticket.

Scaling to warehouse grade

A few practical scaling tips for 2026 edge-first deployments:

Zone sharding: group cameras by logical zones and assign a leader Pi or microserver to act as the local gateway to reduce fan-in. Auto-sharding patterns and connector topologies are discussed in Auto-Sharding Blueprints.
Autoscaling inference: if a single Pi is overloaded, move a camera stream to a nearby idle Pi with k3s + device labels. Reliability and redundancy patterns for Raspberry Pi inference nodes are covered in Edge AI Reliability.
Backpressure: use QoS 1 for MQTT and configure Kafka retention and partitioning by zone to allow parallel consumers.
Model rollouts: Canary models per zone—push a new detector to 5% of nodes, monitor precision/recall before full rollout.

Security & privacy

Keep video on-premise as much as possible and send only structured events. Harden MQTT with TLS and client certificates. Use VLANs and network segmentation to isolate camera networks from corporate VLANs. Mask or blur faces at the edge if PII retention is not required — this reduces compliance burdens and often yields better acceptance from operations teams. For audit and trace patterns that help prove human decisions and maintain compliance, see Designing Audit Trails That Prove the Human Behind a Signature.

Testing, metrics and ROI

Run a 4-week pilot to measure three things: accuracy, latency, and business impact. Key KPIs:

Accuracy: people counting error rate (target < 5% for high-traffic zones)
Latency: time from frame to event < 500ms (edge inference target)
Operational: reduction in manual cycle counts, faster incident response time

Document improvements and proceed zone-by-zone. Often the first wins are in packing and loading docks where people and vehicle flow are most critical.

Troubleshooting checklist

No MQTT events: check TLS certs, client IDs, and Mosquitto persistence files.
High inference latency: reduce camera resolution, lower model complexity, or offload to a nearby more capable Pi temporarily.
False positives on inventory detection: add rule-based filtering and require temporal confirmation across 3 frames.

Advanced strategies & future-proofing (2026+)

Federated learning: aggregate lightweight gradients or model metrics at the central plane to adapt models across zones without shipping raw video. Federated and privacy-preserving patterns tie into edge datastore strategy: Edge Datastore Strategies.
Hybrid inference: run fast detections at the edge and submit low-confidence frames to a cloud validator for higher-cost models.
Event-driven robotics: use the pipeline to trigger AMR pick/route changes when anomalies are detected — the next stage of warehouse automation integration. Scaling and sharding patterns are covered in Auto-Sharding Blueprints.

Real-world example: pilot configuration

A 100k sqft pilot used 12 Pi 5 nodes with AI HAT+ 2 modules across three zones. They ran lightweight detectors at 5 FPS and published aggregated counts every 10s. After 6 weeks the team reduced misplaced pallet incidents by 32% and cut manual spot-count time by 40% — the pilot validated the edge-first approach before a full rollout.

Actionable checklist to get started (15–30 day plan)

Week 1: Acquire 2–3 Pi 5 + AI HAT+ 2 kits and a leader gateway; build base image and network VLANs.
Week 2: Deploy camera + inference container; configure Mosquitto local broker and basic Grafana metrics.
Week 3: Integrate with central Kafka, set up Alertmanager and define 3 anomaly rules.
Week 4: Run the pilot, collect metrics, iterate models and thresholds, and prepare rollout plan for additional zones.

References and further reading

ZDNET coverage of the AI HAT+ 2 (late 2025) for device capabilities and early reviews.
Designing Tomorrow's Warehouse: the 2026 playbook and automation trends highlighted by industry leaders.
Edge AI reliability and redundancy patterns: Edge AI Reliability.

Key takeaways

Edge-first reduces cost and latency: AI HAT+ 2 unlocks affordable on-device inference on Raspberry Pi 5.
MQTT + Kafka is a pragmatic pairing for local resilience with central analytics and long-term storage. Consider connector and sharding patterns from Auto-Sharding Blueprints.
Start small, iterate fast: pilot zones, monitor precision/recall and operational impact before scaling. Storage and hybrid cloud tradeoffs are explained in Distributed File Systems for Hybrid Cloud and Edge‑Native Storage in Control Centers.

Call to action

Ready to deploy a pilot? Grab the starter kit repository with prebuilt images, Docker Compose files and example models at our GitHub starter repo (link in the club resources). Join the Programa.club workshop for a live walkthrough where we provision Pi nodes, tune detectors for warehouse lighting, and wire up alerts to Slack — seats fill fast. Drop a note in the community forum with your warehouse size and goals and we’ll recommend a tailored pilot plan.

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