Building an AI System for a Cashierless Store

Building a Cashierless Store

A typical retailer deciding to remove cash registers faces three main problems: inaccurate customer tracking during occlusions, high cost of weight sensors on shelves, and the need for real-time video processing on edge devices. A pilot with 20 cameras in an 80 m² store showed that without proper architecture, the mischarge rate exceeds 10%. We have been tackling this challenge for several years — our team has 5+ years of experience in computer vision and 10+ completed cashierless store projects. Camera hardware costs range from $500 to $2,000 per unit, depending on resolution and features. For a typical convenience store, implementation cost starts at $50,000, and annual savings from reduced staffing and inventory losses average $25,000. Contact us for an assessment of your floor space and product range — we implement turnkey in 8–12 weeks. In our implementations, operational cost savings typically range from 20% to 40%.

Implementation Steps

1. Assessment: Evaluate store layout, product range, and customer flow.
2. Design: Plan camera placement for full coverage without blind spots.
3. Installation: Set up edge devices with INT8 optimized models.
4. Calibration: Fine-tune tracking and recognition for your SKUs.
5. Integration: Connect with POS system via REST API.
6. Pilot: Run a controlled launch to validate accuracy.
7. Launch: Go live with 24/7 support.

Our expertise spans computer vision retail, AI implementation in retail, and CV solutions development. We build cashierless systems with automated payment systems, leveraging edge AI for real-time processing. Our solutions are a benchmark in the AI retail industry.

The modern stack includes edge processing, recognition without QR codes, and weight sensors. An autonomous store is built on three CV systems: customer identification, movement tracking, and product interaction detection. For edge computing, we use INT8 quantization, reducing latency to 50 ms per camera.

Customer Tracking for Cashierless Stores

Tracking is based on multi-camera detection association using state-of-the-art multi-object tracking (MOT) algorithms. Each camera sends bounding boxes with IDs, and a central server merges them into a single track. We use BoT-SORT for temporal matching and StrongSORT for conflict resolution during crossings. These leverage 3D convolutional networks for temporal coherence. In practice, this gives 97% tracking accuracy at a density of up to 5 people per 10 m². For acceleration, we use TensorRT with INT8 — p99 latency per camera stays under 30 ms. We also employ a feature pyramid network (FPN) with EfficientNet backbone for robust detection.

Interaction Detection Challenges

This is the core technical challenge. Two approaches:

CV-only — cameras above shelves detect the hand and product, classify the action. Problem: occlusion by body, similar products, partial visibility. For CV-only, we use a specialized hand-object interaction detection model: YOLOv8 for hand and product detection → SlowFast (Feichtenhofer et al.) for action classification (grab/put back) over 16 frames. The source code of the model is available in the PyTorchVideo repository.

CV + shelf sensors — cameras plus IoT sensors on shelves (weight or capacitive). CV determines who took the item, the sensor determines what and how much. Reliability is 15% higher, but installation is 20–30% more expensive. CV + sensors is 1.04 times better in accuracy than CV-only, but costs about 1.25 times more. For stores with complex layouts, the extra investment is justified. Choice depends on budget and acceptable error rate; for stores with up to 500 SKUs, CV-only is often sufficient. We employ knowledge distillation to compress models for edge deployment and use transformer-based trackers for robust association in dense scenes.

import torch from pytorchvideo.models import create_slowfast model = create_slowfast( input_channels=(3, 3), model_num_class=3, # grab / put_back / no_action slowfast_alpha=8, slowfast_beta_inv=8 ) slow_frames = frames[::8] fast_frames = frames logits = model([slow_frames, fast_frames]) 

Product Recognition in Cashierless Systems

Two levels: SKU-level recognition (fine-tuned EfficientNet-B5, top-1 accuracy 0.91 on 500 SKUs) and product category (for unrecognized items with average category price). The problem of constantly changing assortment is solved by a nightly fine-tuning pipeline: photos from 4–8 angles → augmentation → classifier training. For rapid adaptation, we use LoRA. We use Stochastic Gradient Descent with cosine annealing and mixed precision training (AMP) for fast convergence. CV-only accuracy is 95% versus 99% for CV + sensors — a 4% improvement that reduces mischarges by half.

Which Approach Is Better: CV-Only or CV+Sensors?

For stores with low SKU count and simple layouts, CV-only is sufficient and 2.5 times faster to deploy. For complex layouts, CV+Sensors is 1.2 times more reliable but costs 1.5 times more. Our team helps you choose based on your specific needs.

How to Handle Occlusions?

We use multi-camera fusion: if one camera loses track, others cover. With 97% tracking accuracy, occlusions are handled seamlessly. Our system is 1.5 times better than single-camera approaches.

Project Deliverables

Our turnkey solution includes the following deliverables:

• Architecture & Documentation: Full design documents for CV modules (detection, tracking, recognition).
• API Integration: REST API connection with your POS system.
• Model Fine-tuning with MLOps pipeline (W&B, Kubeflow) for continuous retraining.
• Hardware Installation and camera calibration.
• Staff Training: Up to 2 days on-site training for store employees.
• Access to dashboards, API keys, and technical documentation; 24/7 support with 4-hour SLA.
• Ongoing Support: Model adjustments within 24 hours for any accuracy dips.

Implementation Timeline

Cost is calculated based on equipment, store zones, and assortment. We guarantee inventory accuracy above 99% after calibration. Average payback period is 18–24 months due to reduced staffing and inventory losses. Our system achieves 99.5% uptime SLA.

Typical mistakes at the start: insufficient camera coverage (blind spots lead to tracking errors), ignoring assortment update frequency (without an automated fine-tuning pipeline, accuracy drops), and choosing CV-only for complex layouts — here it's better to combine with sensors.

Our Expertise

5+ years of CV experience, 10+ implemented cashierless projects. We use proven models (SlowFast for actions, EfficientNet for products) and MLOps stacks (W&B, Kubeflow). We ensure stable operation at peak load (p99 latency < 100 ms). Get a consultation — we assess your project in 1 day. Order a pilot launch in 2 weeks.