In this paper we present a system for the automatic detection and tracking of metallic objects concealed on moving people in sequences of millimetre-wave (MMW) images. The millimetre-wave sensor employed has been demonstrated for use in covert detection because of its ability to see through clothing, plastics and fabrics.The system employs two distinct stages: detection and tracking. In this paper a single detector, for metallic objects, is presented which utilises a statistical model also developed in this paper. The second stage tracks the target locations of the objects using a Probability Hypothesis Density filter. The advantage of this filter is that it has the ability to track a variable number of targets, estimating both the number of targets and their locations. This avoids the need for data association techniques as the identities of the individual targets are not required. Results are presented for both simulations and real millimetre-wave image test sequences demonstrating the benefits of our system for the automatic detection and tracking of metallic objects.
Video-frame-rate millimetre-wave imaging has recently been demonstrated with a quality similar to that of a low-quality uncooled thermal imager. In this paper we will discuss initial investigations into the transfer of image processing algorithms from more mature imaging modalities to millimetre-wave imagery.The current aim is to develop body segmentation algorithms for use in object detection and analysis. However, this requires a variety of image processing algorithms from different domains, including image de-noising, segmentation and motion tracking. This paper focuses on results from the segmentation of a body from the millimetre-wave images and a qualitative comparison of different approaches is presented. Their performance is analysed and any characteristics which enhance or limit their application are discussed.While it is possible to apply image processing algorithms developed for the visible-band directly to millimetrewave images, the physics of the image formation process is very different. This paper discusses the potential for exploiting an understanding of the physics of image formation in the image segmentation process to enhance classification of scene components and, thereby, improve segmentation performance. This paper presents some results from a millimetre-wave image formation simulator, including synthetic images with multiple objects in the scene.
In this paper we present a system for the automatic detection and tracking of metallic objects concealed on moving people in sequences of millimetre-wave (MMW) images. The millimetre-wave sensor employed has been demonstrated for use in covert detection because of its ability to see through clothing, plastics and fabrics. The system employs two distinct stages: detection and tracking. In this paper a single detector, for metallic objects, is presented which utilises a statistical model also developed in this paper. The second stage tracks the target locations of the objects using a Probability Hypothesis Density filter. The advantage of this filter is that it has the ability to track a variable number of targets, estimating both the number of targets and their locations. This avoids the need for data association techniques as the identities of the individual targets are not required. Results are presented for both simulations and real millimetre-wave image test sequences demonstrating the benefits of our system for the automatic detection and tracking of metallic objects.
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