Regional-Based Object Detection Using Polarization and Fisher Vectors in Passive Millimeter-Wave Imaging

Passive millimeter-wave (PMMW) imaging is a powerful approach for detecting hidden objects underneath clothing. The theoretical basis of object detection methods is the contrast of brightness temperature (TB) image. TB differences may be caused by the diversity of material, physical temperature, surface structure, and so on. Existing methods are mainly based on single-polarization and single-pixel processing, which usually generate many discrete pixels of false alarms or missing detections. In this article, we present a regional-based method for hidden object detection using polarization and Fisher vector (FV) features. The necessity of polarization averaging for detection is revealed by theoretical simulation and experimental analyses. Based on the superpixel segmentation of polarization mean image, a modified FV, regional mean FV (RMFV), is created to extract concealed object features. Various imaging experimental data of typical security inspection scenarios are applied to verify the proposed method. The robustness and effectiveness are proved by comparing with several state-of-the-art methods.