Scalable, Privacy-Aware Crowd Flow Visualization

This demonstration presents an innovative crowd flow visualization system tailored for academic makerspaces which leverages low-resolution thermal cameras to balance privacy and robustness.In high-capacity venues like makerspaces, understanding crowd dynamics is crucial for optimizing space utilization.Traditional systems often compromise privacy for functionality, relying on high-definition video, which raises ethical concerns.Our solution employs a network of thermal camera modules, strategically placed to detect crowd flow without capturing identifiable information at any stage in the process.Each module, installed overhead, records crowd flow data using a privacy-aware thermal sensor, which is then transmitted wirelessly to a central server to be compiled and visualized.The demonstration features three interactive components: an in-depth look at the thermal detection algorithm within a single module, a comprehensive visualization of crowd flow across the entire network of thermal camera modules, and an engaging display of light on the module reflecting the density of the area at a glance.Participants will engage with the technology, observing their thermal signatures and understanding how the system processes data in real-time.This setup not only showcases the practicality of our system but also emphasizes its unobtrusive nature, ensuring minimal disruption while providing valuable insights into crowd behavior.Our work aims to establish a scalable model for crowd visualization and managing space in various high-capacity environments, enhancing the overall user experience in academic makerspaces and beyond.