SiVe: See Into the Vehicle's Hidden Persons via Laser Doppler Vibrometer

Detecting stowaways hidden in various transport vehicles, including cars, trucks, containers, and trailers, is crucial to border security inspection systems. Existing solutions mainly rely on contact-based sensors and manual inspection, which significantly compromise the efficiency of border control operations. Therefore, there is an urgent need for an automated, efficient and fast non-contact vehicle hidden person detection system. In this paper, we propose SiVe, a novel border inspection system that utilizes laser Doppler vibrometer (LDV) to detect hidden people in the vehicle. We extract signals associated with human activities (such as breathing, heartbeat, low-frequency body movements, etc.) from complex laser reflection data to detect the presence of hidden people. Specifically, we first employs the Empirical Mode Decomposition (EMD) algorithm to extract and reconstruct signals associated with human activities in complex and noisy environments. Then based on the characteristics of EMD outputs, we design a Time-series Variation Feature extraction Identification network (TVFI-net) model that accurately captures complex time-varying patterns for efficient and reliable detection of hidden people presence. Extensive real-world experiments validate the effectiveness of SiVe in various environments. The system achieves an average presence detection accuracy of 99.93$\%$for sedans and MPVs, 98.37$\%$for light trucks, 98.07$\%$for heavy trucks, and 95.23$\%$for trailers in non-contact detection of hidden people across twelve different vehicle types, under various indoor and outdoor environments.