  Designing an automatic checkout system for retail stores at the human level
accuracy is challenging due to similar appearance products and their various
poses. This paper addresses the problem by proposing a method with a two-stage
pipeline. The first stage detects class-agnostic items, and the second one is
dedicated to classify product categories. We also track the objects across
video frames to avoid duplicated counting. One major challenge is the domain
gap because the models are trained on synthetic data but tested on the real
images. To reduce the error gap, we adopt domain generalization methods for the
first-stage detector. In addition, model ensemble is used to enhance the
robustness of the 2nd-stage classifier. The method is evaluated on the AI City
challenge 2022 -- Track 4 and gets the F1 score $40\%$ on the test A set. Code
is released at the link https://github.com/cybercore-co-ltd/aicity22-track4.
