Active Shooter Detection in Multiple-Person Scenario Using RF-Based Machine Vision

Abstract

Emerging applications of radio frequency (RF) vision sensors for security and gesture recognition primarily target single individual scenarios which restricts potential applications. In this article, we present the design of a cyber-physical framework that analyzes RF micro-Doppler signatures for individual anomaly detection, such as a hidden rifle among multiple individuals. RF avoids certain limitations of video surveillance, such as recognizing concealed objects and privacy concerns. Current RF-based approaches for human activity detection or gesture recognition usually consider single individual scenarios, and the features extracted for such scenarios are not applicable for multi-person cases. From a machine learning perspective, the RF sensor spectrogram images are conducible for training using deep convolutional neural networks. However, generating a large labeled training dataset with an exhaustive variety of multi-person scenarios is extremely time consuming and nearly impossible due to the wide range of combinations possible. We present approaches for multi-person spectrogram generation based on individual person spectrograms that can augment the training dataset and increase the accuracy of prediction. Our results show that the spectrogram generated by RF sensors can be harnessed by artificial intelligence algorithms to detect anomalies such as a concealed weapon for single and multiple people scenarios. The proposed system can aid as a standalone tool, or be complemented by video surveillance for anomaly detection, in scenarios involving single or multiple individuals.