How Data Labeling Services Empower New Retail in 2021?

New Retail

New retail has become a major trend in AI applications. Facing a variety of new retail models, it is not easy to improve tremendously in essence. Based on AI computer vision algorithm, a cloth fashion company can directly recommend a client's unique wear style according to gender, age, face shape, figure, clothing color, silhouette, foot shape and select the most suitable clothes and on the big screen inside the store.

Facial Recognition

The customized “face recognition” algorithm, combined with a store somatosensory camera, can quickly recognize customers’ facial features, establish personalized tags, and give customized clothing suggestions. As the customer enters the special experience area, the somatosensory camera in front of the customer will start work in real-time: locate and analyze the key points, and output the facial feature data report, including the face shape and eye shape.

Based on the image recognition training exercise and feature extraction, the clothing recognition algorithm can automatically recognize and output the clothing color, style, discovering the most suitable style for clients.

Now the algorithm can automatically detect the RGB value and proportion, recognize 8 kinds of silhouette categories such as H-type, V-type, X-type, and A-type, and associate 18 kinds of clothing styles such as commuting, simplicity, etc.

Behind New Retail: Data Labeling Service

What we need to be clear is for AI companies and the entire industry, data annotation is an important part of the realization of artificial intelligence. The accuracy and efficiency of the labeled data affect the final result of the artificial intelligence algorithm model.

Traditional enterprises with intelligent transformation and technology enterprises need the assistance of training data service providers with rich project experience to help sort out the data labeling instruction and to obtain more suitable data. The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.

In the process of in-depth industrial landing, there is still a gap between artificial intelligence technology and enterprise needs. The core goal of enterprise users is to use artificial intelligence technology to achieve business growth. Actually, artificial intelligence technology itself cannot directly solve all the business needs. It needs to create products and services that can be implemented on a large scale based on specific business scenarios and goals.

Common Labeling Tools in New Retail

2D boxing，Polyline，Polygon，Image classification， Semantic segmentation，Video annotation

Common Applications

Object Recognition: Goods on the shelf, goods picking-up

Object Tracking in Video: Holding goods video annotation, personalized shopping

Object Classification: Cosmetics brand classification, clothing color style, and current popular style classification

Regional Segmentation: Different areas segmentation

Other Applications

• Search Relevance: customer behavior analysis, product discovery time shortening, and brand loyalty enhance
• Shelf Management: product misplacement, shortage, and mispricing
• Unmanned Store: tracking of buyers’ behavior and gesture
• Optical Character Recognition
• Inventory Detection
• Spatial Object Tracking & Temporal Segmentation

① Movement, poses and gestures recognition

② Player behavioral analysis

• Receipts, price tags, restaurant menus OCR transcription
• Customer survey, feedback analysis
• Personalized shopping customer’s sentiments analysis

① Virtual trial rooms and racks

② Digital Assistance

End

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Relevant Articles

1 Data Labeling — How to Select a Data Labeling Company

2 Labeling Service — Four Customer Pain Points in Getting No Bias Training Data?

3 No Bias Training Data — the New Bottlenecks in Machine Learning

4 Data Labeling Case Study in New Retail — Cosmetics Brand Classification and Labeling

5 What is Semantic Segmentation, Instance Segmentation, Panoramic segmentation?