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Guides January 28, 2026

Computer Vision for Warehouses

Computer Vision for Warehouses

OneTrack Guide

Computer Vision for Warehouses

Every other sensor gives you a single data point. A temperature reading. A G-force measurement. A location ping. Useful, but limited.

A camera captures everything. Context. Sequence. Cause and effect. What happened, when it happened, and why it happened. That makes cameras the most powerful sensors in the world for understanding physical operations.

The problem was never capturing video. Warehouses have had cameras for decades. The problem was making sense of it.

The Math That Broke Traditional Video

A single forklift generates 8+ hours of footage per shift. Multiply that by 50 forklifts, 3 shifts, 7 days a week. That's over 8,000 hours of video every week from one facility.

No human can watch that. No team can watch that. Traditional video systems became expensive storage solutions. Footage existed, but it only got reviewed after something went wrong. By then, the damage was done.

This is why telematics systems became popular. They reduced the data problem to simple metrics: G-force readings, location pings, speed measurements. Manageable data, but you lost all the context. You knew something happened. You didn't know what actually happened.

A G-force alert tells you there was an impact. It doesn't tell you if the operator was on their phone, if a pedestrian walked into the travel path, if the racking was damaged, or if the product was affected. The single data point creates more questions than it answers.

What Computer Vision Actually Does

Computer vision is a field of artificial intelligence that trains machines to interpret visual information. In a warehouse context, that means teaching systems to watch video and understand what they're seeing.

There are three core capabilities that matter for warehouse operations.

Detection

The system identifies objects and events in the video stream. Is that a forklift? A pedestrian? A pallet? A phone in the operator's hand? Detection is the foundation. You can't analyze what you can't see.

Modern detection models can identify dozens of object types simultaneously. Forklifts, reach trucks, order pickers. Pedestrians with and without vests. Pallets, racking, dock doors. Product types, damage patterns, debris on the floor.

Classification

Once objects are detected, the system classifies what's happening. Is this normal operation or an exception? Is this a near-miss or routine traffic? Is this an impact that caused damage or a gentle touch?

Classification turns raw detections into meaningful events. A pedestrian in a warehouse is normal. A pedestrian within 3 feet of a moving forklift is a near-miss. The difference matters.

Tracking

The system follows objects through space and time. Where did this forklift come from? Where is it going? How long has it been idle? What path did this pallet take from receiving to storage?

Tracking creates continuity. Instead of isolated snapshots, you get complete journeys. You can trace a product from dock to stock. You can follow an operator through an entire shift. You can reconstruct the sequence of events that led to an incident.

Edge Processing: Why Location Matters

Computer vision can run in two places: in the cloud or at the edge (on the device itself). For warehouse operations, edge processing is essential.

Real-Time Response

Cloud processing introduces latency. Video has to travel from the camera to a data center, get processed, and return results. That round trip takes time. For safety applications, time matters. An alert that arrives 30 seconds after a near-miss is a report, not a warning.

Edge processing happens on the device. Neural Processing Units (NPUs) built into modern sensors can run computer vision models in real-time. Detection, classification, and tracking happen as events unfold. Alerts can fire in milliseconds.

Bandwidth and Storage

Raw video is expensive to transmit and store. A single camera generates gigabytes per day. Multiply by hundreds of cameras across a network, and you're looking at infrastructure costs that quickly become prohibitive.

Edge processing solves this by filtering at the source. Instead of streaming everything to the cloud, the system identifies what matters and transmits only that. A 10-second clip of a safety event instead of 8 hours of routine operation. Metadata describing activity patterns instead of raw footage of empty aisles.

Privacy

Not all video should leave the facility. Edge processing allows sensitive footage to be analyzed locally, with only anonymized insights transmitted upstream. This addresses concerns about surveillance while still capturing the operational intelligence that matters.

What You Can See That You Couldn't Before

Computer vision makes visible what was previously invisible. Here are specific examples across the three domains that matter most in warehouse operations.

Safety Events

Traditional safety programs relied on incident reports. Someone had to witness an event and take the time to document it. Most near-misses went unreported. The events that predicted accidents remained hidden.

Computer vision captures every relevant event automatically:

These aren't random samples. They're comprehensive records. Every shift, every operator, every piece of equipment. The difference between tracking a handful of reported incidents and thousands of leading indicators is the difference between reactive and proactive safety management.

Productivity Patterns

Your WMS knows when transactions happen. It doesn't know what happens between transactions. Computer vision fills that gap.

Quality and Compliance

Claims and disputes often come down to conflicting accounts. Computer vision provides proof.

From Video to Ground Truth Data

The output of computer vision isn't video. It's data. Structured, queryable data about what happens in your operation.

This is the real transformation. Video is hard to analyze at scale. Data is easy. Once events are extracted and classified, they flow into analytics systems like any other data source.

You can correlate safety events with operators, shifts, zones, or time periods. You can benchmark productivity across facilities. You can track trends over weeks and months. You can build models that predict where problems will occur before they happen.

This is what we mean by ground truth. Not what someone typed into a system. Not what should have happened according to a plan. What actually happened, captured continuously, verified by video.

When you combine this ground truth data with information from your WMS, LMS, and other systems, you get a complete picture of operations that was previously impossible. The timestamp from your WMS says a pallet was put away at 10:15. The video shows you exactly how it happened, where it went, and what the operator did along the way.

The Three Applications

All of this capability serves three fundamental goals in warehouse operations.

Safety

The goal is to prevent incidents, not just document them. Computer vision enables this by capturing leading indicators. The behaviors and conditions that precede accidents. Phone use, speed violations, pedestrian proximity events. These happen hundreds of times before they result in an injury. When you can see them, you can coach them. When you coach them, you prevent the incidents they cause.

The shift is from reactive investigation to proactive intervention. Instead of reviewing footage after someone gets hurt, supervisors coach on specific behaviors before they cause harm. The evidence is indisputable. Here's the video. Here's what happened. Here's what needs to change.

Productivity

Labor is the largest variable cost in most warehouse operations. Small improvements in productivity translate directly to margin. Computer vision makes those improvements possible by revealing where time actually goes.

Most productivity losses are invisible to traditional systems. The operator who takes 3 minutes between tasks instead of 30 seconds. The forklift that travels an extra 200 feet because of a congested aisle. The dock door that sits empty for 45 minutes between trailers. These losses add up to hours per shift, but they don't show up in your WMS reports.

When you can see the losses, you can address them. Process changes. Zone redesigns. Coaching conversations. Equipment reallocation. The data tells you where to focus for maximum impact.

Quality

Every shipment that leaves your warehouse is your reputation. Damage, shortages, and errors cost money directly through claims and chargebacks. They cost even more through lost customer confidence.

Computer vision provides proof. When a customer claims a shipment was damaged, you have video of how it was loaded. When they claim items were missing, you have footage of every pallet that went on that trailer. When there's a dispute about what happened, you have the evidence.

More importantly, you can identify where quality problems originate in your process. Is damage happening at receiving, put-away, picking, or loading? The data shows you. That means you can fix the root cause instead of just managing claims.

The Implementation Reality

Computer vision for warehouses isn't a science project. The technology has matured to the point where deployment is straightforward. Modern systems install in minutes per device, connect automatically, and start generating insights immediately.

The real challenge isn't technology. It's change management. Having data about what happens in your operation means you have to do something with it. Supervisors need to coach differently. Processes need to change. Accountability needs to exist.

The companies that succeed treat computer vision as an operational platform, not a monitoring tool. The goal isn't to watch people. It's to see the operation clearly enough to improve it. That requires leadership commitment, supervisor training, and a culture that uses data to solve problems rather than assign blame.

The result, when done right, is a facility that operates on reality instead of guesswork. Decisions based on what actually happens instead of what someone thinks happened. Coaching based on evidence instead of opinion. Continuous improvement driven by data instead of intuition.

That's what computer vision enables. Not surveillance. Visibility. The ability to see your operation clearly enough to make it better.

Getting Started

Most organizations start with a specific pain point. Safety incidents they can't explain. Productivity gaps they can't close. Claims they can't defend. Computer vision addresses all three, but you don't have to tackle everything at once.

Pick the problem that matters most. Deploy sensors where that problem occurs. Start seeing what you couldn't see before. Then expand from there.

The technology is ready. The question is whether your operation is ready to see itself clearly.

Book a demo to see computer vision in action in your warehouse.

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