  Multi-class product counting and recognition identifies product items from
images or videos for automated retail checkout. The task is challenging due to
the real-world scenario of occlusions where product items overlap, fast
movement in the conveyor belt, large similarity in overall appearance of the
items being scanned, novel products, and the negative impact of misidentifying
items. Further, there is a domain bias between training and test sets,
specifically, the provided training dataset consists of synthetic images and
the test set videos consist of foreign objects such as hands and tray. To
address these aforementioned issues, we propose to segment and classify
individual frames from a video sequence. The segmentation method consists of a
unified single product item- and hand-segmentation followed by entropy masking
to address the domain bias problem. The multi-class classification method is
based on Vision Transformers (ViT). To identify the frames with target objects,
we utilize several image processing methods and propose a custom metric to
discard frames not having any product items. Combining all these mechanisms,
our best system achieves 3rd place in the AI City Challenge 2022 Track 4 with
an F1 score of 0.4545. Code will be available at
