Egocentric Shopping Cart Localization

This work investigates the new problem of image-based egocentric shopping cart localization in retail stores. The contribution of our work is two-fold. First, we propose a novel large-scale dataset for image-based egocentric shopping cart localization. The dataset has been collected using cameras placed on shopping carts in a large retail store. It contains a total of 19,531 image frames, each labelled with its six Degrees Of Freedom pose. We study the localization problem by analysing how cart locations should be represented and estimated, and how to assess the localization results. Second, we benchmark different image-based techniques to address the task. Specifically, we investigate two families of algorithms: classic methods based on image retrieval and emerging methods based on regression. Experimental results show that methods based on image retrieval largely outperform regression-based approaches. We also point out that deep metric learning techniques allow to learn better visual representations w.r.t. other architectures, and are useful to improve the localization results of both retrieval-based and regression-based approaches. Our findings suggest that deep metric learning techniques can help bridge the gap between retrieval-based and regression-based methods.