Automated Object Recognition using Multiple X-ray Views

Abstract

In order to reduce the security risk of a commercial aircraft, passengers are not allowed to take certain items in their carry-on baggage. For this reason, human operators are trained to detect prohibited items using a manually controlled baggage screening process. The inspection process, however, is highly complex as hazardous items are very difficult to detect when placed in close packed bags, superimposed by other objects, and/or rotated showing an unrecognizable profile. In this paper, we review certain advances achieved by our research group in this field. Our methodology is based on multiple view analysis, because it can be a powerful tool for examining complex objects in cases in which uncertainty can lead to misinterpretation. In our approach, multiple views (taken from fixed points of view, or using an active vision approach in which the best views are automated selected) are analyzed in the detection of regular objects. In order to illustrate the effectiveness of the proposed method, experimental results on recognizing guns, razor blades, pins, clips and springs in baggage inspection are presented achieving around 90% accuracy. We believe that it would be possible to design an automated aid in a target detection task using the proposed algorithm.