2019/01/22 by Xiu-Shen Wei, Wei, Xiu-Shen, Quan Cui +7 · 111 citations
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Neural Network Applications #Artificial intelligence #Benchmark (surveying) #Cartography #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Data mining #Data science #FOS: Computer and information sciences #Product (mathematics) #Quality (philosophy) #Scale (ratio) #Variety (cybernetics) #Visual Attention and Saliency Detection #cs.CV
paper · pdf · doi:10.48550/arxiv.1901.07249
published in arXiv (Cornell University) (Cornell University) · Project page: https://rpc-dataset.github.io/
arxiv created 2019/01/22 · openalex publication_date 2019/01/22 · arxiv updated 2019/01/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Over recent years, emerging interest has occurred in integrating computer vision technology into the retail industry. Automatic checkout (ACO) is one of the critical problems in this area which aims to automatically generate the shopping list from the images of the products to purchase. The main challenge of this problem comes from the large scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous update of the products. Despite its significant practical and research value, this problem is not extensively studied in the computer vision community, largely due to the lack of a high-quality dataset. To fill this gap, in this work we propose a new dataset to facilitate relevant research. Our dataset enjoys the following characteristics: (1) It is by far the largest dataset in terms of both product image quantity and product categories. (2) It includes single-product images taken in a controlled environment and multi-product images taken by the checkout system. (3) It provides different levels of annotations for the check-out images. Comparing with the existing datasets, ours is closer to the realistic setting and can derive a variety of research problems. Besides the dataset, we also benchmark the performance on this dataset with various approaches. The dataset and related resources can be found at \urlhttps://rpc-dataset.github.io/.