# Mask-Aware Spatiotemporal Classification of Millimeter-Wave Radar Point Cloud Sequences Using DGCNN and Transformer for Child–Pet Recognition in Enclosed Spaces

## TL;DR

This paper introduces a new method using radar data and AI to accurately recognize children and pets in enclosed spaces like cars.

## Contribution

A mask-aware spatiotemporal framework combining DGCNN and Transformer for robust millimeter-wave radar point cloud classification.

## Key findings

- The proposed method achieves 97.8% accuracy in classifying children, cats, and dogs.
- The mask mechanism and time series modeling significantly improve robustness in complex environments.
- The framework is deployable and generalizable for enclosed space life form identification.

## Abstract

Applications in enclosed spaces such as vehicle cabin on-site detection, human–pet separation, and pet care have put forward higher requirements for non-contact target recognition. Millimeter-wave radar point clouds have advantages such as privacy friendliness and robustness against low light and occlusion. However, their point clouds are generally sparse, with obvious noise and multipath interference. Moreover, the fluctuation of point numbers over time makes alignment and feature learning difficult, which leads to performance degradation of existing point cloud classification methods in complex environments. To this end, this paper proposes a spatiotemporal joint classification framework for millimeter-wave point cloud sequences: An effective point mask mechanism is introduced in the spatial dimension to suppress the interference of invalid points generated by alignment on the neighborhood composition and feature aggregation and improve the reliability of local geometric representation; and to integrate attention-based time series modeling in the time dimension and enhance category separability by using cross-frame dynamic patterns. The experimental results show that the proposed method can achieve an accuracy rate of 97.8% in the three-classification tasks of Child, Cat and Dog and the ablation analysis verifies the key contributions of the mask mechanism and time series modeling to robust recognition. This framework provides a deployable and more generalized millimeter-wave point cloud solution for the identification of life forms in confined spaces.