Exploiting mmWave and Deep-Learning Models to Estimate People Count in Urban Scenarios

Due to new regulations regarding privacy and the increasing reservations of citizens about surveillance systems, cities are exploring privacy-friendly technologies for crowd monitoring. An alternative gaining significant interest is mmWave radar for people counting applications. However, most studies in mmWave monitoring primarily focus on indoor or outdoor deployments under ideal conditions. Working with the municipality of Ams-terdam, we propose a method for counting people with mmWave in a real outdoor scenario. These realistic scenarios pose two significant difficulties. First, it is hard to estimate the number of people when they walk together because the radar captures the group as a single-point cloud. Second, the dynamic environment adds random points (noise) that may get reflected as false counts. To tackle these challenges, we optimize a state-of-the-art deep learning model to attain noise filtering and cluster disaggregation. We deploy the radar across a street and measure its performance under real traffic conditions. Our system achieves an accuracy of 96% for Presence detection and 69% for People counting. Over the long term, the system can monitor the crowd level with an error below 2.5%. Furthermore, considering the needs of our application, we reduce the complexity of the original SoA model by 93%. Our results indicate that mmWave is a promising solution for privacy-friendly people counting in urban scenarios and exposes unique challenges that have not been reported or considered in the SoA.