Change detection for detecting potential threats in forward-looking video and downward-looking ground-penetrating radar data

Forward looking video can provide a large amount of tactically-relevant information to vehicle operators regarding roadside explosive threats. However it is difficult for vehicle operators to keep track of what roadside objects have changed since their last excursion, or what new objects have appeared on a road and might therefore contain an explosive threat. Furthermore, the large amount of data generated by forward looking video can overwhelm users. It would be of benefit to vehicle operators if only objects that had significantly changed since a recent excursion were flagged and presented to the user. In this work we develop techniques for video and ground-penetrating radar (GPR) tracking and aligning, and novel-object identification for application in route clearance patrols. We focus on aligning video data collected using vehicle mounted forward-looking video cameras and downward-looking GPR using locallyinvariant feature transforms and set-based distance metrics. Based on these aligned image streams, we then apply pattern classification approaches to discriminate new explosive threats from stationary and persistent objects. The techniques described in this work are widely applicable to other forward and downward-looking sensor systems, and are computationally tractable. The results indicate the potential to robustly identify recently changed roadside threats, and to present a significantly reduced amount of information to end-users for further operational analysis. © 2011 SPIE.

Duke Scholars

Author Leslie M. Collins Electrical and Computer Engineering