Smart self-checkout system
A Smart Self-Checkout System is an automated checkout solution used in retail stores that allows customers to scan and bag their items themselves, without the need for a cashier. Smart Self-Checkout Systems are becoming increasingly important in today's retail industry as they offer several benefits to both retailers and customers. They help reduce labor costs for retailers, improve checkout speed and convenience for customers, and provide valuable data and analytics on customer behavior and purchasing patterns. This challenge set aims to find innovative solutions to automatic checkout to allow customers to walk out of a store without having to interact with any kind of cashier. The objective is to develop a system that can identify the goods in a shopping cart and allow customers to walk out of the store without having to checkout. The system should be able to identify different types of goods in a store. Participants can leverage technologies such as artificial intelligence, machine learning, augmented reality, internet of things, gesture recognition, touchscreen technology, computer vision, and voice recognition to develop a software solution. They should focus on building solutions that are innovative, user-friendly, scalable, secure, reliable, energy efficient, customizable, fast, and should be able to integrate with CPOS’s existing systems. The judges, including CPOS officials, will evaluate the solutions based on their impact, innovation, feasibility, and user experience.
Features for the Smart-Checkout System
1. Personalization: Implement a system that recognizes repeat customers and provides personalized recommendations based on their purchase history and preferences
2. User-Friendly Interface and Experience: Develop a multi-platform application with an intuitive user interface that leverages touchscreen technology, natural language processing (NLP), andvoice recognition.
3. Integration with Multiple Payment Options: Implement a payment gateway that supports a wide variety of payment methods, including credit/debit cards, mobile wallets, digital currencies, and QR code-based transactions.
4. Inventory Management and Real-Time Data: : Integrate an IoT-based inventory management system that employs RFID, barcode scanning, weight sensors or computer vision technologies to track items in real-time. Utilize AI and machine learning algorithms to analyze sales data and generate actionable insights for retailers.
5. Anti-Theft and Security Measures: Combine computer vision and AI-based object detection with RFID tagging to identify potential theft incidents. Implement a facial recognition system that alerts security personnel when a known shoplifter enters the store..
Participants are encouraged to think outside the box and create a smart self-checkout system that revolutionizes the retail experience in smart cities, promoting efficiency, sustainability, and convenience for both retailers and customers.
Select one of the 3 technologies and develop a solution with 2 or more features mentioned above.
Use gesture recognition technology to allow customers to scan and bag items simply by waving their hands over the scanner, reducing the need for physical contact with the system.
Participants can develop a machine learning-based model that can identify fraudulent activities based on data patterns. The model can be trained on a dataset of phishing, fraud, and digital scam incidents to identify common patterns and signatures of these scams.
Develop an augmented reality (AR) app that assists customers in finding and scanning items, providing real-time information about product features and prices.