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

Abstract

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 a anchor-free object detection network called CircleDet to detect passengers' 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% on our own metro object detection (MOD) dataset.

On Edge Crowd Traffic Counting System using Deep Learning on Jetson Nano for Smart Retail Environment

“…Another study by Zhongxing et al, [9] Real-Time Enumeration of Metro Passenger Volume Using Anchor-Free Object Detection Network on Edge Devices. They record footage from the cameras and create CircleDet, an anchor-free object detection network, to identify passengers' heads.…”