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Detecting stationary human targets in FLIR imagery

In the military arena, intelligent unmanned ground vehicles (UGVs), weighing 10 tons or more, may be designed and used for transportation or combat purposes. To ensure safe operations among civilians and friendly combatants, it is crucial for these UGVs to detect and avoid humans who might be injured unintentionally. In this paper, a multi-stage detection algorithm for stationary humans in forward-looking infrared (FLIR) imagery is proposed. This algorithm first applies an efficient feature-based anomalies detection algorithm to search the entire input image, which is followed by an eigen-neural-based clutter rejecter that examines only the portions of the input image identified by the first algorithm, and culminates with a simple evidence integrator that combines the results from the two previous stages. The proposed algorithm was evaluated using a large set of challenging FLIR images and the results support the usefulness of this multi-stage architecture.