RadHARSimulator V2: Video to Doppler Generator

TL;DR

RadHARSimulator V2 introduces a flexible, video-based Doppler spectrum generator for radar human activity recognition, combining computer vision and radar modules with neural networks to improve simulation accuracy and effectiveness.

Contribution

This paper presents RadHARSimulator V2, a novel simulator that generates Doppler spectra directly from video footage, integrating computer vision and radar modules with a hybrid neural network architecture.

Findings

The simulator effectively generates realistic Doppler spectra from videos.

The proposed neural network improves radar-based HAR accuracy.

Numerical experiments validate the simulator's and network's effectiveness.

Abstract

Radar-based human activity recognition (HAR) still lacks a comprehensive simulation method. Existing software is developed based on models or motion-captured data, resulting in limited flexibility. To address this issue, a simulator that directly generates Doppler spectra from recorded video footage (RadHARSimulator V2) is presented in this paper. Both computer vision and radar modules are included in the simulator. In computer vision module, the real-time model for object detection with global nearest neighbor is first used to detect and track human targets in the video. Then, the high-resolution network is used to estimate two-dimensional poses of the detected human targets. Next, the three-dimensional poses of the detected human targets are obtained by nearest matching method. Finally, smooth temporal three-dimensional pose estimation is achieved through Kalman filtering. In radar module, pose interpolation and smoothing are first achieved through the Savitzky-Golay method. Second, the delay model and the mirror method are used to simulate echoes in both free-space and through-the-wall scenarios. Then, range-time map is generated using pulse compression, moving target indication, and DnCNN. Next, Doppler-time map (DTM) is generated using short-time Fourier transform and DnCNN again. Finally, the ridge features on the DTM are extracted using the maximum local energy method. In addition, a hybrid parallel-serial neural network architecture is proposed for radar-based HAR. Numerical experiments are conducted and analyzed to demonstrate the effectiveness of the designed simulator and the proposed network model. The open-source code of this work can be found in: https://github.com/JoeyBGOfficial/RadHARSimulatorV2-Video-to-Doppler-Generator.