  Automatic Check-Out (ACO) receives increased interests in recent years. An
important component of the ACO system is the visual item counting, which
recognizes the categories and counts of the items chosen by the customers.
However, the training of such a system is challenged by the domain adaptation
problem, in which the training data are images from isolated items while the
testing images are for collections of items. Existing methods solve this
problem with data augmentation using synthesized images, but the image
synthesis leads to unreal images that affect the training process. In this
paper, we propose a new data priming method to solve the domain adaptation
problem. Specifically, we first use pre-augmentation data priming, in which we
remove distracting background from the training images using the coarse-to-fine
strategy and select images with realistic view angles by the pose pruning
method. In the post-augmentation step, we train a data priming network using
detection and counting collaborative learning, and select more reliable images
from testing data to fine-tune the final visual item tallying network.
Experiments on the large scale Retail Product Checkout (RPC) dataset
demonstrate the superiority of the proposed method, i.e., we achieve 80.51%
checkout accuracy compared with 56.68% of the baseline methods. The source
codes can be found in https://isrc.iscas.ac.cn/gitlab/research/acm-mm-2019-ACO.
