DIAT-μRadHAR: Radar micro-Doppler Signature dataset for Human Suspicious Activity Recognition

In the view of national security, radar micro-Doppler (m-D) signatures-based recognition of suspicious human activities becomes significant. In connection to this, early detection and warning of terrorist activities at the country borders, protected/secured/guarded places and civilian violent protests is mandatory. Designing an automated human suspicious activities: army crawling, army jogging, jumping with holding a gun, army marching, boxing, and stone-pelting/grenades-throwing, recognition system using a suitable deep convolutional neural network (DCNN) model is rapidly growing due to its inherent in-depth features extraction capability. As a value addition to this research, an X-band continuous wave (CW) 10 GHz radar has been developed at our radar systems laboratory and used to acquire the m-D signatures, to prepare a dataset (DIAT-μRadHAR) corresponding to above mentioned suspicious activities. In order to prepare a realistic dataset, human targets of different heights, weights, and gender are directed to perform the suspicious activities in front of the radar at different ranges between 10 m - 0.5 km and at different target aspect angles (0°, ±15°, ±30° and ±45°).

在国家安全的视角下，基于雷达微多普勒（m-D）特征的人可疑活动识别显得尤为重要。与此相关，对国家边境、保护区、守卫场所以及民间暴力抗议的早期检测与预警成为必须。利用适合的深度卷积神经网络（DCNN）模型设计自动化的可疑人类活动识别系统，包括匍匐前进、慢跑、持枪跳跃、行军、拳击和投掷石块/手榴弹等，其因内在的深度特征提取能力而迅速发展。作为本研究的增值部分，我们的雷达系统实验室已开发出一台X波段连续波（CW）10 GHz雷达，用于获取m-D特征，并准备了一个（DIAT-μRadHAR）数据集，以对应上述可疑活动。为了准备一个逼真的数据集，不同身高、体重和性别的目标人物被引导在雷达前方不同距离（10米至0.5公里）以及不同目标方位角（0°、±15°、±30°和±45°）处执行可疑活动。

搜集汇总

背景与挑战

背景概述

DIAT-μRadHAR数据集是一个专门用于识别人类可疑活动的雷达微多普勒签名数据集，包含3780个频谱图图像，覆盖六种活动类型。数据在不同距离（10米至0.5公里）和角度（0°至±45°）下采集，以提高模型的泛化能力。