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USE CASE

Retail Object Tracking Dataset

Tracking goods on video from self-service checkouts
Computer Vision The ability to recognize and analyze images and videos
Retail Using machine learning in retail
Object Detection The determination of an object's position by marking it with a bounding box
Classification Ability to assess and distinguish objects based on predefined criteria
15 000
photos
3
weeks

CASE DESCRIPTION

Tracking of goods at self-checkout registers using Bounding Box in CVAT

Additional classification of goods based on payment status, product type, and cost category

The data is used for training self-checkout registers in identifying goods

APPLICATION AREAS OF THE DATASET

Payment control:

Detection of product payment to detect suspicious behavior and fight shoplifting

Campaign effectiveness:

Tracking customer actions at the cashier to assess the popularity of adjacent advertising stands

Self-service improvement:

Tracking customer movements to identify issues with scanning and payment of products

FACTORS AFFECTING THE FINAL PROJECT COST INCLUDE

Our data quality guarantee is 95%. For annotation orders requiring quality above 95%, we offer enterprise solutions

Why
Training Data

  • Quality Assurance:
  • Enhanced Data Accuracy
  • Consistency in Labels
  • Reliable Ground Truth
  • Mitigation of Annotation Biases
  • Cost and Time Efficiency
  • Data Security and Confidentiality:
  • GDPR Compliance
  • Non-disclosure agreement
  • Data Encryption
  • Multiple data storage options
  • Access Controls and Authentication
  • Expert Team:
  • 6 years in industry
  • 35 top project managers
  • 40+ languages
  • 100+ countries
  • 250k+ assessors
  • Flexible and Scalable Solutions:
  • 24/7 availability of customer service
  • 100% post payment
  • $550 minimum check
  • Variable Workload
  • Customized Solutions

Team leads project

Anton Tseluiko
Operations manager
Arthur Kazukevich
Python-developer
Daria Yurkevich
Quality Control Manager
woman

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