Operator-assisted Threat Assessment : Adaptation of a Focus-of-attention Technique to the Identification of Potential Threat Regions in Carry-on Baggage Imagery

The Oak Ridge National Laboratory (ORNL) has adapted and evaluated an image-based approach for assisting x-ray baggage screening personnel in identifying potential threat items in x-ray imagery of carryon baggage. The technique seeks to identify threat items including guns, knives, grenades, pipebombs, and improvised explosive devices. ORNL’s methodology has focused on identifying and testing the region-based image characteristics that could potentially indicate a threat object. ORNL’s focus-ofattention (FOA) technique breaks with traditional patternor template-based techniques for object identification by analyzing small, local spatial regions and the neighborhood relationships of these regions through a decision method based on historical precedence through a training process. Regions that could potentially contain items that pose a threat are assessed and displayed to an operator with a confidence value for the assessed threat. Operator assisted technologies such as the ORNL’s FOA, represent a necessary step in the evolution of reliable and accurate inspection systems that achieve full automation. INTRODUCTION AND BACKGROUND In a set of recommendations made to the Federal Aviation Administration (FAA) in July of 1997, the National Materials Advisory Board advocated the investigation of methods to automatically detect weapons in carry-on and checked baggage. Besides advocating the implementation of weapon detection capability for x-ray carry-on baggage systems, the board further recommended that future security systems minimize the role of human operators in monitoring and detection and instead capitalize on unique human capabilities in the resolution of alarm [1]. This use of image processing and pattern recognition to detect weapons or threatening objects is not a simple task. For the carry-on baggage inspection task, single view single energy x-ray systems were originally employed based on costs and throughput requirements. The mapping of randomly oriented 3-D objects under inspection to the 2-D xray image displayed to the screener are not optimal conditions for classical scene analysis; methodologies and techniques relying on precise segmentation, template matching or shape recognition have demonstrated poor performance to date. These techniques are further hampered by the wide variety of objects found in carry-on baggage, the random orientation of objects, and the possibility that important objects are completely or partially occluded by denser innocuous objects. An attempt in the early 90s to identify guns and several other weapons in x-ray imagery had very limited success partially because of the reliance on weapon shape and the difficulty of identifying objects that were positioned in poor perspective relative to the scanning beam [2]. Techniques that have relied on template matching and other object segmentation approaches to recognition are ineffective and typically result in the detection of false positives or negatives in the data To enhance the ability of x-ray inspection equipment operators to determine what they are viewing, over the last decade manufacturers have developed x-ray based baggage inspection systems using dual R.K. F. (Correspondence): Email: ferrellrk@ornl.gov; WWW: http://www-ismv.ic. ornl.gov; Telephone: (865) 574-5730; Fax: (865) 5746-8380. Prepared by the Oak Ridge National Laboratory, Oak Ridge, Tennessee, 37831-6285, operated by UT-Battelle, LLC for the U.S. Department of Energy under contract DE-AC05-00OR22725.