國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles./
作者:	Bharati, Sushil Pratap.
面頁冊數:	1 online resource (125 pages)
附註:	Source: Masters Abstracts International, Volume: 57-05.
Contained By:	Masters Abstracts International57-05(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (125 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355777949Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles.
LDR:03254ntm a2200349Ki 4500 001 919628
005 20181129115239.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780355777949
035 $a (MiAaPQ)AAI10745393
035 $a (MiAaPQ)ku:15732
035 $a AAI10745393
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Bharati, Sushil Pratap. $3 1194243
245 1 0 $a Vision Based Adaptive Obstacle Detection, Robust Tracking and 3D Reconstruction for Autonomous Unmanned Aerial Vehicles.
264 0 $c 2018
300 $a 1 online resource (125 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 57-05.
500 $a Adviser: Guanghui Wang.
502 $a Thesis (M.S.)--University of Kansas, 2018.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Automotive engineering. $3 1104081
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0540
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Kansas. $b Electrical Engineering & Computer Science. $3 1194244
773 0 $t Masters Abstracts International $g 57-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10745393 $z click for full text (PQDT)