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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Self-service bag drop efficiently assists passengers to check-in their baggage in the airport. Nevertheless, the baggage appearance transportability cannot be accurately detected by existing self-service bag drop equipment. We plan to adopt a convolutional neural network with video input to detect the appearance transportability of baggage. However, public baggage picture datasets are captured in the daily background, thus existing approaches trained on these datasets achieve imprecise performance for airport self-service bag drop. We introduce a new dataset for airport self-service bag drop named ASS-BD and a novel sequential hierarchical sampling multi-object tracker. Most of the video clips that comply with the consignment regulations were recorded in the airport scene. Video clips that do not comply with the consignment regulations were recorded in the laboratory simulation scene. A sequential hierarchical sampling multi-object tracking baseline is adopted to solve some problematic frames due to part occlusion, rare pose, and motion blur. We conduct experiments to demonstrate that our dataset is suitable for the airport self-service bag drop scenario. Our approach is capable of the inspection task of air baggage appearance transportability in real-time.

Airline Baggage Appearance Transportability Detection Based on A Novel Dataset and Sequential Hierarchical Sampling CNN Model