Real-Time Warehouse Safety Through Computer Vision

Unsafe lifting postures were the leading cause of workplace injuries at a national tire distributor's large-scale warehouse network. Traditional safety monitoring -- periodic walkthroughs and incident-based reviews -- could not catch biomechanically risky behavior in real time. OSHA recordable rates were climbing, and the client needed a proactive system that could detect unsafe behavior as it happened.

Euclid deployed CAML (Computer-Aided Machine Learning), a GPU-accelerated computer vision platform running across 200+ on-premises cameras. The system achieves 95% pose detection accuracy with sub-2-second alerting, transforming existing camera infrastructure into an active safety monitoring system.

About the Client

The client operates a nationwide network of large-scale distribution warehouses, processing thousands of heavy tire shipments daily. Their workforce performs repetitive manual lifting, stacking, and transport tasks in environments where safety is paramount.

With hundreds of workers across multiple facilities, traditional safety monitoring -- periodic walkthroughs and incident-based reviews -- was no longer sufficient to prevent injuries before they happened.

The Challenge

No real-time safety signals. Rising OSHA recordables.

Unsafe lifting postures were the leading cause of workplace injuries. Workers frequently adopted biomechanically risky positions -- deep bending, asymmetric loads, overhead reaches -- that traditional supervision couldn't catch in real time.

Unsanctioned zone entry compounded the risk: workers routinely entered restricted areas near forklifts and conveyor systems without detection. There were no automated signals to flag these violations as they occurred.

OSHA recordable rates were climbing, and the client needed a proactive system -- one that could detect unsafe behavior in real time, not just document incidents after the fact.

Our Approach

CAML: computer-aided machine learning for workplace safety.

Phase 1: On-Premises GPU Infrastructure

Deployed dedicated GPU compute nodes within the client's warehouse network -- ensuring all video data stays on-premises and meets strict data residency requirements.

Phase 2: TensorRT-Optimized Pose Estimation

Built a custom 18-point skeletal pose estimation model optimized for NVIDIA TensorRT, achieving 95% accuracy while maintaining real-time inference speeds across 200+ concurrent camera feeds.

Phase 3: Rule-Based Safety Engine

Defined biomechanical safety thresholds for lifting postures, bending angles, and zone boundaries -- translating ergonomic science into programmable detection rules.

Phase 4: Real-Time Alerting Pipeline

Built a sub-2-second alert pipeline from camera frame to supervisor notification -- including pose classification, rule evaluation, and multi-channel delivery (visual dashboards, mobile alerts, PA system triggers).

Phase 5: Audit Trail and Analytics

Every detection event is logged with timestamped video, skeletal overlay, and zone metadata -- creating a complete audit trail for OSHA compliance reviews and trend analysis.

Outcomes

Proactive safety, measurable results:

• 95% pose detection accuracy -- across 200+ cameras
• 60% faster unsafe posture detection -- sub-2-second alerts
• Reduced OSHA recordables -- measurable safety improvement
• Full audit trail compliance -- every incident documented
• 3 unknown high-risk zones identified -- previously undetected
• 200+ cameras running real-time analytics -- on-premises GPU-accelerated

What Comes Next

CAML's detection capabilities revealed three previously unknown high-risk zones within the warehouse network -- areas where unsafe postures and zone violations clustered disproportionately. These insights are now driving targeted workflow redesigns.

The next phase focuses on ergonomic workstation redesigns in these high-risk zones, shift-pattern optimization based on fatigue analytics, and expanding the platform to additional distribution facilities across the client's national network.