Radar-Based Crowd Counting in Real-World Environments With Spatiotemporal Transformer

Abstract

With the advent of deep learning (DL) for signal processing, the deployment of DL for radar-based crowd counting has yielded significant performance enhancement. Despite these advancements, current methodologies predominantly undergo validation in controlled conditions with limited subject movement variability, posing a challenge for practical usage. Addressing this gap, this letter first attempts the application of radar-based crowd counting in an unregulated and dense setting, capturing the radar reflections of up to 31 subjects in real-world scenarios, such as queues at restaurant kiosks. Furthermore, to address the complexities of such a challenging condition, we introduce a novel radar crowd counting model that utilizes a spatiotemporal transformer. The expremental results demonstrate the potentiality of the proposed model as a robust crowd counting system under the full realistic scenarios, as well as establish its superiority over the conventional radar-based crowd counting models.