Blue Yonder Group, Inc.

Tata Consultancy Services Limited

Ramakrishna Perla

Mayank Tiwari

Ehtesham Hassan

Ramya Sugnana Murthy Hebbalaguppe

Vineet Chaudhary

Arun Raj Parwana Adiraju

Bharani Sarvepalli

Archie Gupta

Ramya Sugnana Hebbalaguppe

A system and method are disclosed for computer vision in-store planogram analysis. The system includes a retail entity comprising a product display area defined by a planogram, and a planogram analyzer comprising a server which trains a machine learning model to generate recommendations to optimize one or more planogram metrics using historical data and computer vision data. The system generates, by the trained machine learning model, recommendations to optimize the one or more planogram metrics, and alters the planogram based on the one or more recommendations. The system further uses the trained machine learning model with a probabilistic optimization technique approximating a global optimum to generate the one or more recommendations to optimize the one or more planogram metrics. The computer vision includes one or more of eyeball movement, eyeball engagement and eyeball lingering time.

Computer vision in-store planogram analysis

A system and method of automatic product attribute recognition receive training images having bounding boxes associated with one or more products in the training images, receive attribute values for each of the one or more products in the training images, and train a first convolutional neural network (CNN) model to generate bounding boxes for and identify each of the one or more products with the training images until the accuracy of the first CNN model is above a first predetermined threshold. The system and method further train a second CNN model for each of the products associated with the cropped images until the second CNN generates attribute values for the one or more attributes with an accuracy above a second predetermined threshold, and automatically recognize the one or more attributes for a new product image by presenting the product image to the first and second CNN models.

System and method of visual attribute recognition

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Hyderabad Telangana, India

Ramakrishna Perla