Gaussian mixture models for measuring local change down-track in LWIR imagery for explosive hazard detection

摘要

Burying objects below the ground can potentially alter their thermal properties. Moreover, there is often soil disturbance associated with recently buried objects. An intensity video frame image generated by an infrared camera in the medium and long wavelengths often locally varies in the presence of buried explosive hazards. Our approach to automatically detecting these anomalies is to estimate a background model of the image sequence. Pixel values that do not conform to the background model may represent local changes in thermal or soil signature caused by buried objects. Herein, we present a Gaussian mixture model-based technique to estimate the statistical model of background pixel values. The background model is used to detect anomalous pixel values on the road while a vehicle is moving. Foreground pixel confidence values are projected into the UTM coordinate system and a UTM confidence map is built. Different operating levels are explored and the connected component algorithm is then used to extract islands that are subjected to size, shape and orientation filters. We are currently using this approach as a feature in a larger multi-algorithm fusion system. However, in this article we also present results for using this algorithm as a stand-alone detector algorithm in order to further explore its value in detecting buried explosive hazards.