University of Illinois Urbana-Champaign

Real-time aerial vehicle detection and tracking with depth-aided vision sensing

Zhang, Bicheng

Content Files
ZHANG-THESIS-2015.pdf

Permalink

https://hdl.handle.net/2142/88127

Description

Title
Real-time aerial vehicle detection and tracking with depth-aided vision sensing
Author(s)
Zhang, Bicheng
Issue Date
2015-07-24
Director of Research (if dissertation) or Advisor (if thesis)
Dullerud, Geir E.
Department of Study
Mechanical Science & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2015-09-29T20:38:57Z
Keyword(s)
  • real-time
  • aerial vehicles
  • drone
  • depth sensor
  • Kinect
  • object recognition
  • tracking
  • detection
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.
Graduation Semester
2015-8
Type of Resource
text
Permalink
http://hdl.handle.net/2142/88127
Copyright and License Information
Copyright 2015 Bicheng Zhang

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