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Title:Real-time aerial vehicle detection and tracking with depth-aided vision sensing
Author(s):Zhang, Bicheng
Advisor(s):Dullerud, Geir E.
Department / Program:Mechanical Science & Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
aerial vehicles
depth sensor
object recognition
Abstract:We study the problem of detecting and tracking flying objects in real-time with color and depth images. We improve the sparse part-based representation learning approach by utilizing depth data from depth vision sensor to achieve much faster detection speed while maintain high detection accuracy. We revised some of algorithms presented in part-based representation method to get marginally better performance. Then we invented a novel data preprocessing method, which is based on edge detection and contour selection to generate possible vehicle locations before the image is processed by classifier. This approach can be applied to any object with distinguishable parts in relatively fixed spatial configurations, and our target here is the flying vehicle at indoor environment. Since flying objects tend to change poses and locations fast and frequently, the detection algorithm needs to run fast so that the tracking algorithm can keep on tracking the detected object. We also use hardware acceleration tools to further increase algorithm speed. The results of vehicle localization and tracking are shown and a critical evaluation of our approaches is also presented.
Issue Date:2015-07-24
Rights Information:Copyright 2015 Bicheng Zhang
Date Available in IDEALS:2015-09-29
Date Deposited:August 201

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