"Multiple Object Tracking with Background Estimation in Hyperspectral Video Sequences"
Kandylakis Z., Karantzalos K., Doulamis A., Doulamis N., 2015, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).
Although, cutting-edge frame hyperspectral sensors can currently acquire hypercubes at video rates with low spatial resolution, they offering enhanced discrimination capabilities for the characterization of subtle spectral features and important object reflectance properties. To this end, a generic framework was designed, developed and validated for multiple object tracking in hyperspectral video sequences. The background estimation was efficiently addressed through advanced scale space filtering and dimensionality reduction. The detection of the moving objects was performed on the reduced representation for low computational complexity. The object recognition task was based on certain spectral and geometric features which were associated with a rule-based classification. The experimental results appear promising and indicate the efficiency of the developed approach.
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