2026-09-20T17:42:46Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/88127 2023-07-11 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

Dullerud, Geir E. Zhang, Bicheng 2015-09-29T20:38:57Z 2015-09-29T20:38:57Z 2015-08 2015-07-24 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. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-09-29 without embargo terms The student, Bicheng Zhang, accepted the attached license on 2015-07-24 at 11:57. The student, Bicheng Zhang, submitted this Thesis for approval on 2015-07-24 at 12:28. This Thesis was approved for publication on 2015-07-24 at 16:03. DSpace SAF Submission Ingestion Package generated from Vireo submission #8644 on 2015-09-29 at 13:23:43 Made available in DSpace on 2015-09-29T20:38:57Z (GMT). No. of bitstreams: 2 ZHANG-THESIS-2015.pdf: 2508623 bytes, checksum: f0779aebc45729b3849fa0dd02046093 (MD5) LICENSE.txt: 4210 bytes, checksum: 6c9b34f8defe39260ef1801a271b2a08 (MD5) Previous issue date: 2015-07-24 application/pdf http://hdl.handle.net/2142/88127 en Copyright 2015 Bicheng Zhang real-time aerial vehicles drone depth sensor Kinect object recognition tracking detection Real-time aerial vehicle detection and tracking with depth-aided vision sensing text text 2015-8 Mechanical Science & Engineering Mechanical Engineering University of Illinois at Urbana-Champaign Thesis M.S.