Safer and Smarter: Contactless shopping with on-device object detection. A look at Google’s latest research on an improved approach to local product recognition
In these unprecedented times involving a worldwide pandemic, many changes have taken place in our daily lives. To ensure the safety and well being of all, many protocols and guidelines have been implemented across society, such as maintaining a minimum distance or limiting physical contact with exposed and shared surfaces.
Businesses are getting creative by adapting their shopping experiences to ensure the safety of their customers and employees. Contactless services have become more of a necessity than a fad, and technology has evolved to keep pace with these newfound demands. Many stores have standardized Scan & Go technology, which allows customers to scan item barcodes and pay using just their smartphones, thus limiting human contact and providing a faster, simpler alternative.
Progress in computer vision, combined with the portability of smartphones, has led to newer and safer approaches to traditional shopping methods.
Dedicated barcode scanners that need to be shared among store employees are now being switched out for smartphone apps, and augmented reality is playing a bigger and bigger role in new store management approaches, transforming routine tasks such as counting inventory and restocking shelves. Scandit is one such app that is using ML-powered methods like text and object recognition to power contactless working in shops and stores.
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Deep Computer Vision is capable of doing object detection and image classification task. In image classification tasks, the particular system receives some input image.
Implementations regarding all of above experiments alongside the different result plots are provided in GitHub repository.
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