Retail Shelf Monitoring System

A real-time retail shelf monitoring solution powered by computer vision and machine learning. The system leverages existing CCTV infrastructure to automatically detect out-of-stock (OOS) situations and product misplacements, enabling data-driven shelf management and faster restocking decisions.

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🚀 Key Features

• Real-time Shelf Analysis using YOLOv12-M for product detection
• SKU Recognition via MobileNetV3 embeddings and FAISS similarity search
• Planogram Compliance with automated grid generation and SKU matching
• Tracking with SORT for temporal consistency across frames
• Multi-threaded Architecture for high throughput and low-latency inference
• Cross-platform Optimization with ONNX, OpenVINO, and TensorRT
• Interactive Desktop App (PySide6) for visualization and alert management

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⚙️ Processing Pipeline

CCTV Stream → Keyframe Selection → Shelf Detection →
Image Alignment → YOLOv12 Detection → SKU Recognition →
Grid Mapping → Temporal Consensus → Alert Generation →
Desktop UI Display → Staff Confirmation

Keyframe-based processing minimizes redundant computation, while tracking (SORT + Kalman Filter) ensures temporal stability across frames.

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🧩 Core Components

• Detection Model: YOLOv12-M fine-tuned on SKU-110k (mAP50–95 = 0.56)
• SKU Recognition: 93% Top-1 accuracy on custom embeddings
• Grid Generation: Automated DBSCAN-based clustering of shelf items
• Alert Logic: Temporal consensus filtering to reduce false positives
• App Interface: Live feed overlay, planogram visualization, alert confirmation

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🧰 Technologies

• Python, PyTorch, OpenVINO, TensorRT, OnnxRuntime, Pytorch-TensorRT, FAISS, PySide6, PostgreSQL, Redis
• Tracking: SORT + Hungarian Algorithm
• Feature Matching: ORB/SIFT with RANSAC-based homography

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Documentation

• Technical Report: Comprehensive system implementation details
• Project Tree: Current development status and architecture overview

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🚀 Quick Start

Installation

# Clone repository
git clone https://github.com/Alijanloo/Retail-Shelf-Monitoring.git
cd Retail-Shelf-Monitoring

uv pip install -e .

# Configure settings
cp config.example.yaml config.yaml
# Edit config.yaml with your preference

# Start PostgreSQL and Redis services
docker-compose up -d

# Launch desktop application
uv run python launch_ui.py

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.