标题:Optimal Threshold and LoG Based Feature Identification and Tracking of Bat Flapping Flight
作者:Lin, Yousi; Xu, Yang; Chen, Hui; Bender, Matthew J.; Abbott, A. Lynn; Mueller, Rolf
通讯作者:Lin, YS;Lin, YS
作者机构:[Lin, Yousi; Xu, Yang; Chen, Hui] Shandong Univ, Sch Informat Sci & Engn, Jinan, Peoples R China.; [Lin, Yousi; Abbott, A. Lynn] Virginia Tech, Brad 更多
会议名称:17th IEEE Winter Conference on Applications of Computer Vision (WACV)
会议日期:MAR 24-31, 2017
来源:2017 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2017)
出版年:2017
页码:418-426
DOI:10.1109/WACV.2017.53
摘要:Flapping flight observed in bats offers a promising model for bio-inspiration of small air vehicles because of their high maneuverability, load carrying capacity, and energy efficiency. However, the flight mechanics of bats is very complex due to the highly articulated wing skeleton and the anisotropic, internally-actuated wing membrane. As a result of these complexities, the shape of bat wings can deform quickly and substantially which causes periodic occlusions and large baseline nonlinear deformations of point trajectories in image space. Tracking these points in image space is difficult because the resolution of the images (720x1280) and the frame rate (120Hz) used in these experiments are substantially lower than those used historically. This paper presents a computational approach that utilizes a novel combination of Laplacian-of-Gaussian (LoG) filtering and optimal threshold segmentation to locate markers in images. Using images from 32 cameras, our technique achieved an average hit rate of 83%, with an average false rate of 12%. Our algorithm is shown to perform better than other techniques, including those based on SIFT or LoG filtering alone. In addition to the improved feature detection algorithm, optical flow based tracking is bootstrapped with a spatially recursive unscented Kalman filter to track the identified points during state estimation. The spatially recursive estimator returns as many or more correct correspondences when compared to the standard unscented Kalman filter.
收录类别:CPCI-S
WOS核心被引频次:1
资源类型:会议论文
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