  Recognition of grocery products in store shelves poses peculiar challenges.
Firstly, the task mandates the recognition of an extremely high number of
different items, in the order of several thousands for medium-small shops, with
many of them featuring small inter and intra class variability. Then, available
product databases usually include just one or a few studio-quality images per
product (referred to herein as reference images), whilst at test time
recognition is performed on pictures displaying a portion of a shelf containing
several products and taken in the store by cheap cameras (referred to as query
images). Moreover, as the items on sale in a store as well as their appearance
change frequently over time, a practical recognition system should handle
seamlessly new products/packages. Inspired by recent advances in object
detection and image retrieval, we propose to leverage on state of the art
object detectors based on deep learning to obtain an initial productagnostic
item detection. Then, we pursue product recognition through a similarity search
between global descriptors computed on reference and cropped query images. To
maximize performance, we learn an ad-hoc global descriptor by a CNN trained on
reference images based on an image embedding loss. Our system is
computationally expensive at training time but can perform recognition rapidly
and accurately at test time.

Computer Vision and Pattern Recognition