Feature aided tracking with hyperspectral imagery
Joshua Blackburn, Michael J. Mendenhall, Andrew C. Rice, Paul J. Shelnutt, Neil A. Soliman, Juan Andres Vásquez-Peralvo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
Target tracking in an urban environment presents a wealth of ambiguous tracking scenarios that cause a kinematic-only tracker to fail. Partial or full occlusions in areas of tall buildings are particularly problematic as there is often no way to correctly identify the target with only kinematic information. Feature aided tracking attempts to resolve problems with a kinematic-only tracker by extracting features from the data. In the case of panchromatic video, the features are often histograms, the same is true for color video data. In the case where tracks are uniquely different colors, more typical feature aided trackers may perform well. However, a typical urban setting has similar size, shape, and color tracks, and more typical feature aided trackers have no hopes in resolving many of the ambiguities we face. We present a novel feature aided tracking algorithm combining two-sensor modes: panchromatic video data and hyperspectral imagery. The hyperspectral data is used to provide a unique fingerprint for each target of interest where that fingerprint is the set of features used in our feature aided tracker. Results indicate an impressive 19% gain in correct track ID with our hyperspectral feature aided tracker compared to the baseline performance with a kinematic-only tracker.