13 May 2010 Hyperspectral object tracking using small sample size
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Abstract
This paper introduces a simple approach for object tracking using hyperspectral (HS) spectral features. The approach addresses the object tracking problem using a small object sample size. For a particular application, the key challenges are: (i) Offline training cannot be utilized; (ii) motor vehicles of interest (targets) have a small sample size (e.g., less than 9); and (iii) kinematic states of targets cannot be used for tracking, since stationary targets are also of interest. Using HS imagery, this paper introduces a method that exploits the mean and median averages spectra to estimate higher moments of the underlying (and unknown) probability distribution function of spectra; in particular, skew tendency and sign. Tracking HS targets is then possible using this algorithm to test a sequence of HS imagery, given that target spectra are initially cued by the user. The approach was implemented into a commercially off the shelf workstation, featuring the IBM Cell Processor and GA-180 Add in Board. Preliminary results are promising using a challenging HS data cube.
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Dalton Rosario, Henry Kling, "Hyperspectral object tracking using small sample size", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76950I (13 May 2010); doi: 10.1117/12.850359; https://doi.org/10.1117/12.850359
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KEYWORDS
Detection and tracking algorithms

Statistical analysis

Target detection

Kinematics

Feature extraction

Sensors

Image processing

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