Dense People Counting Using IR-UWB Radar with a Hybrid Feature Extraction Method

TL;DR

This paper introduces a hybrid feature extraction method combining curvelet transform and distance bin analysis for IR-UWB radar-based people counting, achieving over 97% accuracy in dense scenarios.

Contribution

The paper proposes a novel hybrid feature extraction approach for IR-UWB radar that improves counting accuracy in crowded environments.

Findings

Random forest classifier achieved over 97% accuracy.

Hybrid features outperform cluster, activity, and CNN features.

Method is effective in scenarios with up to 20 people.

Abstract

People counting is one of the hottest issues in sensing applications. The impulse radio ultra-wideband (IR-UWB) radar has been extensively applied to count people, providing a device-free solution without illumination and privacy concerns. However, performance of current solutions is limited in congested environments due to the superposition and obstruction of signals. In this letter, a hybrid feature extraction method based on curvelet transform and distance bin is proposed. 2-D radar matrix features are extracted in multiple scales and multiple angles by applying the curvelet transform. Furthermore, the distance bin is introduced by dividing each row of the matrix into several bins along the propagating distance to select features. The radar signal dataset in three dense scenarios is constructed, including people randomly walking in the constrained area with densities of 3 and 4 persons per square meter, and queueing with an average distance of 10 centimeters. The number of people is up to 20 in the dataset. Four classifiers including decision tree, AdaBoost, random forest and neural network are compared to validate the hybrid features, and random forest performs the highest accuracies of all above 97% in three dense scenarios. Moreover, to ensure the reliability of the hybrid features, three other features including cluster features, activity features and CNN features are compared. The experimental results reveal that the proposed hybrid feature extraction method exhibits stable performance with significantly superior effectiveness.