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Intel® Automated Self-Checkout Reference Package

🔄 Package Integration Notice
The Automated Self-Checkout functionality has been consolidated into the Intel® Loss Prevention Reference Package for a unified retail computer vision platform.

What This Means for You

  • Existing Users: Your automated self-checkout use cases are now supported in the Loss Prevention package
  • New Users: Start directly with the Loss Prevention package for the latest features
  • Migration: No code changes needed - simply use the new package location

Why Computer Vision for Retail?

Automated self-checkout systems process complex visual data through multiple stages to transform raw video into actionable business insights:

  1. Video Ingestion: Capture customer interactions and product movements in real-time
  2. Object Detection: Identify products and items using YOLOv5 models
  3. Classification: Categorize and verify items with EfficientNet algorithms
  4. Analytics: Generate loss prevention data and checkout validation

The pipeline below demonstrates this workflow, where video data flows through preprocessing, dual AI model inference (YOLOv5 and EfficientNet), and post-processing to generate metadata and visual bounding boxes for each frame.

Vision Data Flow

This unified platform simplifies deployment complexity with pre-configured, hardware-optimized workflows that scale from pilot programs to enterprise-wide implementations.

Integration Benefits

The automated self-checkout functionality has been consolidated into the Intel® Loss Prevention Reference Package, providing a unified platform for retail computer vision solutions. This integration offers several advantages:

  • Unified Platform: Single application supporting both loss prevention and automated self-checkout use cases
  • Hardware Optimization: Pre-configured workloads optimized for Intel® CPU, GPU, and NPU hardware
  • Flexible Deployment: Multiple workload configurations including:
  • Object Detection (CPU/GPU/NPU)
  • Object Detection & Classification (CPU/GPU/NPU)
  • Age Prediction & Face Detection (CPU/GPU/NPU)
  • Heterogeneous configurations
  • Simplified Management: Single codebase, unified configuration, and streamlined deployment process

What You Want to Do

🚀 I'm New to Intel Retail Solutions

Quick Start (15 minutes): Loss Prevention Getting Started Guide - Set up your environment - Run your first automated self-checkout demo
- Understand the basic workflow

⚙️ I Want to Customize the Solution

Advanced Configuration (30-60 minutes): Loss Prevention Advanced Guide - Customize workload configurations - Optimize for your hardware setup - Configure multiple detection models

📊 I Need Performance Data

Benchmark & Optimize: Loss Prevention Performance Guide - Compare CPU/GPU/NPU performance - Optimize for your specific use case - Understand throughput metrics