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    Automatic Detection, Segmentation and Tracking of Vehicles in Wide-Area Aerial Imagery

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    Author
    Gao, Xin
    Issue Date
    2016
    Keywords
    Electrical & Computer Engineering
    Advisor
    Rodriguez, Jeffrey J.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Object detection is crucial for many research areas in computer vision, image analysis and pattern recognition. Since vehicles in wide-area images appear with variable shape and size, illumination changes, partial occlusion, and background clutter, automatic detection has often been a challenging task. We present a brief study of various techniques for object detection and image segmentation, and contribute to a variety of algorithms for detecting vehicles in traffic lanes from two low-resolution aerial video datasets. We present twelve detection algorithms adapted from previously published work, and we propose two post-processing schemes in contrast to four existing schemes to reduce false detections. We present the results of several experiments for quantitative evaluation by combining detection algorithms before and after using a post-processing scheme. Manual segmentation of each vehicle in the cropped frames serves as the ground truth. We classify several types of detections by comparing the binary detection output to the ground truth in each frame, and use two sets of evaluation metrics to measure the performance. A pixel classification scheme is also derived for spatial post-processing applied to seven detection algorithms, among which two algorithms are selected for sensitivity analysis with respect to a range of overlap ratios. Six tracking algorithms are selected for performance analysis for overall accuracy under four different scenarios for sample frames in Tucson dataset.
    Type
    text
    Electronic Thesis
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Electrical & Computer Engineering
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

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