Hyperspectral imaging and association phenomenology of pedestrians in a cluttered urban environment

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Title: Hyperspectral imaging and association phenomenology of pedestrians in a cluttered urban environment
Author: Herweg, Jared
Abstract: Remote hyperspectral imaging (HSI) has shown promise in several applications such as object detection and tracking. Typically research has focused on large objects, such as vehicles, for tracking due to the spatial resolution of current operational HSI systems. This research seeks to extend the utility of applying HSI to human pedestrian detection using the reflective solar spectral range between 400 - 2500 nm. A phenomenological investigation of a novel scheme to differentiate between pedestrians is studied. By applying the basics of detection theory, this research focuses on being able to differentiate between pedestrians, as well as background materials. Specifically, this research explores the likelihood of detecting and differentiating pedestrians based on four defined subregions comprised of the exposed hair, skin, and the fabrics used for shirts and trousers. The scope of this work encompassed detecting a pedestrian of interest outdoors among other pedestrians in an urban environment consisting of a mixture of asphalt, concrete, grass, and trees. Two unique datasets were created during the course of this effort. One dataset was a collection of fully ground-truthed hyperspectral images of pedestrians in an urban environment. A second dataset was a synthetic rendering of the real-world ground truthed pedestrian scene developed using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Subregion separability analysis results, using spectral reflectance data, provided strong evidence that combining the observable spectral features of detectable subregions is a viable means of distinguishing between pedestrians. Further analysis using real-world HSI data demonstrated that the detection and classification of the pedestrian subregions when changes in illumination, location, and background occur within the field of view of a hyperspectral sensor is achievable with a greater than 60% accuracy. In addition to the direct detection and association analysis using the full spectral range, trade-offs in using spectral subsets of the reflectance spectrum were explored for their utility in detecting and classifying each of the pedestrian subregions. The results suggested that the clothing worn on a pedestrian's torso is the dominant feature for classification and either using a full spectral range (400 - 2500 nm) with 152 spectral bands or the visible to near-infrared spectral range (400 - 1000 nm) with 39 bands provides similar capability to teh full spectral range for distinguishing among pedestrians with similar skin and clothing types.
Record URI: http://hdl.handle.net/1850/15358
Date: 2012-08-17

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