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The detection of fireballs streaks in astronomical imagery can be carried outby a variety of methods. The Desert Fireball Network--DFN--uses a network ofcameras to track and triangulate incoming fireballs to recover meteorites withorbits. Fireball detection is done on-camera, but due to the design constraintsimposed by remote deployment, the cameras are limited in processing power andtime. We describe the processing software used for fireball detection underthese constrained circumstances. A cascading approach was implemented, wherebycomputationally simple filters are used to discard uninteresting portions ofthe images, allowing for more computationally expensive analysis of theremainder. This allows a full night's worth of data; over 1000 36 megapixelimages to be processed each day using a low power single board computer. Thealgorithms chosen give a single camera successful detection large fireball rateof better than 96 percent, when compared to manual inspection, althoughsignificant numbers of false positives are generated. The overall networkdetection rate for triangulated large fireballs is estimated to be better than99.8 percent, by ensuring that there are multiple double stations chances todetect one fireball.

Fireball streak detection with minimal CPU processing requirements for the Desert Fireball Network data processing pipeline