mmFlux: Crowd Flow Analytics with Commodity mmWave MIMO Radar
Anurag Pallaprolu, Winston Hurst, and Yasamin Mostofi

TL;DR
mmFlux is a novel framework that uses commodity mmWave MIMO radar to analyze crowd flow patterns, infer crowd semantics, and generate high-fidelity flow graphs for complex crowd behaviors.
Contribution
The paper introduces a new signal processing pipeline and graph-based analysis method for crowd flow and semantics using mmWave radar, achieving high accuracy in complex scenarios.
Findings
High-fidelity flow field reconstruction for complex crowds
Accurate inference of crowd semantics like turns and dispersions
Effective analysis of crowd dynamics with commodity radar
Abstract
In this paper, we present mmFlux: a novel framework for extracting underlying crowd motion patterns and inferring crowd semantics using mmWave radar. First, our proposed signal processing pipeline combines optical flow estimation concepts from vision with novel statistical and morphological noise filtering. This approach generates high-fidelity mmWave flow fields-compact 2D vector representations of crowd motion. We then introduce a novel approach that transforms these fields into directed geometric graphs. In these graphs, edges capture dominant flow currents, vertices mark crowd splitting or merging, and flow distribution is quantified across edges. Finally, we show that analyzing the local Jacobian and computing the corresponding curl and divergence enables extraction of key crowd semantics for both structured and diffused crowds. We conduct 21 experiments on crowds of up to 20…
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Taxonomy
TopicsAnomaly Detection Techniques and Applications · Indoor and Outdoor Localization Technologies · Autonomous Vehicle Technology and Safety
