Publications
Fusion of image data for beyond-the-fence intruder detection and assessment
The use of combined imagery from different imaging sensors has the potential to provide significant performance improvements over the use of a single image sensor for beyond-the-fence detection and assessment of intruders. Sensing beyond the fence is very challenging for imagers due to uncertain dynamic and harsh environmental conditions. The use of imagery from varying spectral bands can alleviate some of this difficulty by providing stronger truth data that can be combined with truth data from other spectral bands to increase detection capabilities. Imagery fusion of collocated, aligned sensors covering varying spectral bands [1,2,3] has already been shown to improve the probability of detection and the reduction of nuisance alarms. The development of new multi-spectral sensing algorithms that incorporate sensors that are not collocated will enable automated sensor-based detection, assessment, localization, and tracking in harsh dynamic environments. This level of image fusion will provide the capability of creating spatial information about the intruders. In turn, the fidelity of sensed activities is increased resulting in opportunities for greater system intelligence for inferring and interpreting these activities and formulating automated responses. The goal of this work is to develop algorithms that will enable the fusion of multi-spectral data for improved detection of intruders and the creation of spatial information that can be further used in assessment decisions.