In wilderness search and rescue, objects not native or typical to a scene may provide clues that indicate the recent presence of the missing person. This paper presents the results of augmenting an aerial wilderness search-and-rescue system with an automated spectral anomaly detector for identifying unusually colored objects. The detector dynamically builds a model of the natural coloring in the scene and identifies outlier pixels, which are then filtered both spatially and temporally to find unusually colored objects. These objects are then highlighted in the search video as suggestions for the user, thus shifting a portion of the user's task from scanning the video to verifying the suggestions. This paper empirically evaluates multiple potential detectors then incorporates the best-performing detector into a suggestion system. User study results demonstrate that even with an imperfect detector users' detection increased significantly. Results further indicate that users' false positive rates did not increase, though performance in a secondary task did decrease. Furthermore, users subjectively reported that the use of detector-based suggestions made the overall task easier. These results suggest that such suggestion-based systems for search can increase overall searcher performance but that additional external tasks should be limited.
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