Crowd Analytics with a Single mmWave Radar

This paper presents a novel approach for crowd analytics using a single monostatic mmWave radar. We propose a new mathematical model that infers the crowd size for dynamic and quasi-dynamic crowd behaviors. More specifically, we derive a novel closed-form mathematical expression that describes the statistical dynamics of undercounting due to crowd shadowing. This new methodical finding allows for significantly improved crowd density estimates. For spatially-patterned crowds where the mathematical solution does not extend, we then develop a Temporal Convolutional Network (TCN) which is purely trained on simulated data. We perform extensive testing over a total of 22 experiments, with up to (and including) 21 people and in 4 different areas, including indoors, and the proposed mathematical solution achieves a Mean Absolute Error (MAE) of 1.53. Lastly, we show how our framework can infer anomalies, bottlenecks, and crowd engagement level. Overall, the paper can have a significant impact on crowd management and urban planning.