Suspicious Object Detection for Millimeter-Wave Images With Multi-View Fusion Siamese Network

Millimeter-wave (MMW) imaging techniques have been widely used in the public security industries for their under-controlled privacy concerns and no health hazards. However, since MMW images are low resolution and most objects are small, reflection-weak, diverse, suspicious object detection in the MMW images is a very challenging task. This paper develops a robust suspicious object detector for the MMW images based on the Siamese network integrated with the pose estimation and image segmentation, which estimates the coordinates of human joints and segments the complete human images into symmetrical body part images. Unlike most existing detectors, which detect and recognize suspicious objects in MMW images and require a complete training set with correct annotations, our proposed model aims to learn the similarity between two symmetrical human body part images segmented from the complete MMW images. Furthermore, to decrease the misdetection caused by the restricted field of view, we further fuse the multi-view MMW images observed from the same person by designing a decision-level fusion strategy and feature-level fusion strategy based on the attention mechanism. Experimental results on the measured MMW images show that our proposed models have favorable detection accuracy and speed in practical application and thus prove their effectiveness.