Network discovery using wide-area surveillance data

Network discovery of clandestine groups and their organization is a primary objective of wide-area surveillance systems. An overall approach and workflow to discover a foreground network embedded within a much larger background, using vehicle tracks observed in wide-area video surveillance data is presented and analyzed in this paper. The approach consists of four steps, each with their own specific algorithms: vehicle tracking, destination detection, cued graph exploration, and cued graph detection. Cued graph exploration on the simulated insurgent network data is shown to discover 87% of the foreground graph using only 0.5% of the total tracks or graph´s total size. Graph detection on the explored graphs is shown to achieve a probability of detection of 87% with a 1.5% false alarm probability. We use wide-area, aerial video imagery and a simulated vehicle network data set that contains a clandestine insurgent network to evaluate algorithm performance. The proposed approach offers significant improvements in human analyst efficiency by cueing analysts to examine the most significant parts of wide-area surveillance data.