Track every forklift, pallet, person, across every camera in your facility

The Problem

Warehouses and large facilities have cameras everywhere, for security, for compliance, for documentation. They almost never use that camera footage operationally. The reason: tracking what happens across multiple cameras is hard. Within a single camera, tracking is straightforward. Across cameras, you need re-identification (matching the same object as it leaves one camera’s view and enters another’s), and that’s a hard CV problem.

The cost shows up in operations: lost / mis-routed pallets, accidents that nobody can reconstruct, security incidents requiring hours of camera-by-camera review. The data exists; it’s just not connected.

Multi-camera tracking with re-identification connects the cameras. Forklifts, pallets, people, vehicles, tracked continuously across the facility, with trajectories that go through camera boundaries cleanly. Operations gets a real-time map of what’s where.

What the Solution Does

A multi-camera tracking system for warehouses, distribution centers, and large facilities.

1. Detection, per camera, detect objects of interest (forklifts, pallets, people, vehicles).
2. Within-camera tracking, track each object’s movement within a single camera’s view.
3. Cross-camera re-identification, match objects across cameras as they move through the facility.
4. Trajectory aggregation, combine into facility-wide movement maps.
5. Operational insights, alerts, analytics, incident reconstruction.

Where It Fits

This makes sense if you…

• Operate a warehouse / facility / yard with multiple cameras (10+)
• See operational cost from lost / mis-routed objects, accidents, or compliance violations
• Have stable camera placements with reasonable coverage
• Want operational analytics on top of security footage

This is probably not the right time if you…

• Operate at small scale where single-camera tracking suffices
• Need person-identification (not what we build; we track anonymously)
• Have unstable camera setups where coverage changes frequently

Business Value

Operational visibility. Real-time map of what’s where in the facility. Where forklifts are, where pallets sit, where people congregate.

Incident reconstruction. Take a workplace accident, a missing pallet, or a security event. Reconstruction becomes minutes of querying the system. The old way was hours of camera-by-camera review.

Loss reduction. Mis-routed or missing items get detected quickly. We typically see a 30-60% reduction in loss incidents after deployment, depending on the facility.

Compliance documentation. Continuous tracking creates audit trails for safety compliance, traceability, etc.

How It Works

1. Detection

YOLO-family object detection per camera. Object types depend on use case: forklifts, pallets, people, trucks, packages.

2. Within-camera tracking

Track each detected object across frames within a single camera. Standard CV tracking (DeepSORT, ByteTrack, similar).

3. Cross-camera re-identification

The hard part. When an object leaves one camera’s view, identify it when it appears in another’s. We use:

• Appearance embeddings: visual features that persist across camera angles.
• Temporal constraints: known travel time between camera zones rules out implausible matches.
• Floor-plan logic: physical layout informs which camera transitions are possible.

This reuses the architecture from our visual-customer-analytics Re-ID work. It is deliberately not face-based, for both privacy and accuracy.

4. Trajectory aggregation

Per-object trajectories across the facility. Stored as time-series with location stamps.

5. Alerts and analytics

Real-time alerts on configured patterns (object enters restricted zone, dwell time exceeded, expected route deviation). Historical analytics for incident review and operational optimization.

Stack

YOLO-family detection and custom Re-ID models (appearance embeddings) run on Jetson-class edge compute, per camera or per group. A central tracking service ties them together. Datapipe runs the data pipeline, and Metabase or PowerBI drive the analytics dashboards.

What You Need to Make This Work

Data. Floor plan with camera positions. Sample footage covering typical operations.

Integrations. Camera feeds (RTSP / API). Integration with warehouse management system for object identification.

Hardware. Cameras (existing CCTV often works if coverage is sufficient). Jetson edge compute. Network for video streams (substantial bandwidth, local processing helps).

Team. Operations lead. IT contact for camera access. Facility manager for floor-plan and operational understanding.

Implementation Roadmap

1. Test (5-8 weeks)

Pick a zone (one warehouse area with 5-10 cameras). Train detection and Re-ID for your specific object types. Validate against ground truth. Output: working tracking in the test zone with measured accuracy.

2. Pilot (3-5 months)

Expand to full facility. Build operational dashboards. Wire up alerting. Output: facility-wide deployment with documented business outcomes.

3. Production (6-10 months)

Multi-facility rollout. Continuous improvement on edge cases.

Keep in Mind

• Cross-camera Re-ID is probabilistic. Some tracking will be wrong. Accuracy is typically 85-94%, depending on coverage, and we surface the confidence score on each match.
• Camera coverage gaps create tracking gaps. If an object passes through an un-covered area, the system has to guess at the other side.
• Lighting variance degrades Re-ID. Different cameras have different lighting; matching across them is harder.
• Compute requirement is non-trivial. Many cameras running real-time detection and Re-ID needs serious compute.
• Privacy considerations. People-tracking has regulatory implications in many jurisdictions. Pallet and equipment tracking is less constrained.
• Identity persistence over long times is hard. A person who leaves the facility and returns may not be re-identified. From a privacy perspective, that is often the right outcome.

FAQ

Can this work with existing CCTV?

Sometimes, depends on resolution and coverage. Often new cameras or camera repositioning required for production-grade tracking.

Forklift tracking vs. pallet tracking?

Different problems with shared infrastructure. Forklifts are large and consistently visible; pallets are small and often occluded. We tune per object type.

What about RFID or beacons?

Sensor fusion is the right architecture for high-accuracy tracking. CV alone handles most cases; RFID / beacons add precision for critical objects. We integrate.

Person-tracking, privacy?

Anonymous tracking only. Re-ID works from body and clothing, never the face. We discuss jurisdictional compliance during the pilot.

Ready to Discuss?

If you operate a large warehouse or facility and operational visibility is a real cost line, this is a worthwhile pilot. We’ll walk through your facility and your camera infrastructure, and tell you what to expect.

Discuss your project