Work

Crowd Analytics with a Single mmWave Radar

Year: 2024

Type: conference-paper

Abstract: 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.

Source: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

DOI: 10.1145/3636534.3690664

Authors: Anurag Pallaprolu, Phillip Peng, Shaan Sandhu, Winston Hurst, Yasamin Mostofi

Institution: University of California, Santa Barbara

Language: English

Topic: Radar Systems and Signal Processing

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Funders: National Science Foundation, Office of Naval Research