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Vision Based Adaptive Obstacle Detec...

Bharati, Sushil Pratap.

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Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles./
Author:	Bharati, Sushil Pratap.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	125 p.
Notes:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10745393
ISBN:	9780355777949

Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles.
Bharati, Sushil Pratap.

Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 125 p.

Source: Masters Abstracts International, Volume: 57-05.

Thesis (M.S.)--University of Kansas, 2018.

Vision-based autonomous navigation of UAVs in real-time is a very challenging problem, which requires obstacle detection, tracking, and depth estimation. Although the problems of obstacle detection and tracking along with 3D reconstruction have been extensively studied in computer vision field, it is still a big challenge for real applications like UAV navigation. The thesis intends to address these issues in terms of robustness and efficiency. First, a vision-based fast and robust obstacle detection and tracking approach is proposed by integrating a salient object detection strategy within a kernelized correlation filter (KCF) framework. To increase its performance, an adaptive obstacle detection technique is proposed to refine the location and boundary of the object when the confidence value of the tracker drops below a predefined threshold. In addition, a reliable post-processing technique is implemented for an accurate obstacle localization. Second, we propose an efficient approach to detect the outliers present in noisy image pairs for the robust fundamental matrix estimation, which is a fundamental step for depth estimation in obstacle avoidance. Given a noisy stereo image pair obtained from the mounted stereo cameras and initial point correspondences between them, we propose to utilize reprojection residual error and 3-sigma principle together with robust statistic based Qn estimator (RES-Q) to efficiently detect the outliers and accurately estimate the fundamental matrix. The proposed approaches have been extensively evaluated through quantitative and qualitative evaluations on a number of challenging datasets. The experiments demonstrate that the proposed detection and tracking technique significantly outperforms the state-of-the-art methods in terms of tracking speed and accuracy, and the proposed RES-Q algorithm is found to be more robust than other classical outlier detection algorithms under both symmetric and asymmetric random noise assumptions.

ISBN: 9780355777949Subjects--Topical Terms:

649834
Electrical engineering.

Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles.
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100 1 $a Bharati, Sushil Pratap. $0 (orcid)0000-0002-9103-6388 $3 3353174
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 125 p.
500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Guanghui Wang.
502 $a Thesis (M.S.)--University of Kansas, 2018.
520 $a Vision-based autonomous navigation of UAVs in real-time is a very challenging problem, which requires obstacle detection, tracking, and depth estimation. Although the problems of obstacle detection and tracking along with 3D reconstruction have been extensively studied in computer vision field, it is still a big challenge for real applications like UAV navigation. The thesis intends to address these issues in terms of robustness and efficiency. First, a vision-based fast and robust obstacle detection and tracking approach is proposed by integrating a salient object detection strategy within a kernelized correlation filter (KCF) framework. To increase its performance, an adaptive obstacle detection technique is proposed to refine the location and boundary of the object when the confidence value of the tracker drops below a predefined threshold. In addition, a reliable post-processing technique is implemented for an accurate obstacle localization. Second, we propose an efficient approach to detect the outliers present in noisy image pairs for the robust fundamental matrix estimation, which is a fundamental step for depth estimation in obstacle avoidance. Given a noisy stereo image pair obtained from the mounted stereo cameras and initial point correspondences between them, we propose to utilize reprojection residual error and 3-sigma principle together with robust statistic based Qn estimator (RES-Q) to efficiently detect the outliers and accurately estimate the fundamental matrix. The proposed approaches have been extensively evaluated through quantitative and qualitative evaluations on a number of challenging datasets. The experiments demonstrate that the proposed detection and tracking technique significantly outperforms the state-of-the-art methods in terms of tracking speed and accuracy, and the proposed RES-Q algorithm is found to be more robust than other classical outlier detection algorithms under both symmetric and asymmetric random noise assumptions.
590 $a School code: 0099.
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650 4 $a Automotive engineering. $3 2181195
650 4 $a Artificial intelligence. $3 516317
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