Crowd-control is an emerging problem in urban areas and cameras are commonly seen as the solution, however, there are major concerns regarding privacy. To overcome these issues, while still maintaining the ability to keep track of people, mmWave sensors can be utilized instead, but this introduces new challenges when it comes to people counting. They work by recording the data as point clouds, making it difficult to determine the real number of people when they are occluded. To address this challenge, we combine the spatial and temporal information of the point clouds into graphs, and explore the possibilities of Graph Neural Networks. Our system classifies up to 5 people and bikes with an accuracy of 80.47%. This accuracy is 2.53% worse than the state of the art people counting system for mmWave radar point clouds.