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Gül Varol

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Proceedings Article | 4 March 2015 Paper

Toward retail product recognition on grocery shelves

Gül Varol, Rıdvan Kuzu

Proceedings Volume 9443, 944309 (2015) https://doi.org/10.1117/12.2179127

KEYWORDS: Image segmentation, Visualization, Photography, RGB color model, Image fusion, Feature extraction, Object recognition, Image classification, Image processing, Cameras

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This paper addresses the problem of retail product recognition on grocery shelf images. We present a technique for accomplishing this task with a low time complexity. We decompose the problem into detection and recognition. The former is achieved by a generic product detection module which is trained on a specific class of products (e.g. tobacco packages). Cascade object detection framework of Viola and Jones [1] is used for this purpose. We further make use of Support Vector Machines (SVMs) to recognize the brand inside each detected region. We extract both shape and color information; and apply feature-level fusion from two separate descriptors computed with the bag of words approach. Furthermore, we introduce a dataset (available on request) that we have collected for similar research purposes. Results are presented on this dataset of more than 5,000 images consisting of 10 tobacco brands. We show that satisfactory detection and classification can be achieved on devices with cheap computational power. Potential applications of the proposed approach include planogram compliance control, inventory management and assisting visually impaired people during shopping.

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