English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

VISTA: Vision Transformer enhanced by U-Net and Image Colorfulness Frame Filtration for Automatic Retail Checkout