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Methodologies for incorporating the uncertainties characteristic ofdata-driven object detectors into object tracking algorithms are explored.Object tracking methods rely on measurement error models, typically in the formof measurement noise, false positive rates, and missed detection rates. Each ofthese quantities, in general, can be dependent on object or measurementlocation. However, for detections generated from neural-network processedcamera inputs, these measurement error statistics are not sufficient torepresent the primary source of errors, namely a dissimilarity between run-timesensor input and the training data upon which the detector was trained. To thisend, we investigate incorporating data uncertainty into object tracking methodssuch as to improve the ability to track objects, and particularly those whichout-of-distribution w.r.t. training data. The proposed methodologies arevalidated on an object tracking benchmark as well on experiments with a realautonomous aircraft.

Incorporating Data Uncertainty in Object Tracking Algorithms