Real-time Queue Detection Using an Omnidirectional Camera
Yuki Kasahara, Shohei Yokoyama · 2021
Omnidirectional cameras, which can capture an image that covers a 360-degree view, have been widely used in recent years. This study proposes a method to detect human queues in images captured with omnidirectional cameras. The method can be applied to further research in human flow analysis and congestion estimation. In this study, we analyzed the real space using panoramic images captured by an omnidirectional camera. These images contain distortions because the spherical image captured is converted into a flat image, making feature detection difficult. Previous studies focused on object detection and position estimation; our study, however, focused on detecting the direction of a person to enable the detection of a queue. Our results show that straight one-directional queues are detectable, but queues in different formations present problems.