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

Enumeration of metro passenger volume is essential in providing effective passenger guidance and improving the usage rate of each carriage. However, existing methods cannot provide the accurate number of alighting and boarding passengers at each gate on the platform. For existing visual methods, the occlusion problem seriously affected the results caused by the angle of view. In this study, we introduce a real-time metro passenger volume enumerating algorithm that is simple, effective, and fast enough to run on edge devices mounted above the platform gate. First, we capture videos from the cameras and design an anchor-free object detection network called CircleDet to detect passengers&#x2019; heads. CircleDet predicts a circle to localize and bound the target instead of traditional bounding box. Then, we apply a simple but effective circle IoU-based method to identify and track passengers in the videos. CircleDet can achieve up to 111 frames per second (FPS) running on NVIDIA RTX 2080 and 7.8 FPS on an NVIDIA Jetson Nano device. The accuracy of enumeration is as high as 97.1&#x0025; on our own metro object detection (MOD) dataset.

Real-Time Enumeration of Metro Passenger Volume Using Anchor-Free Object Detection Network on Edge Devices