Optimal Threshold and LoG Based Feature Identification and Tracking of Bat Flapping Flight

Abstract

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 (720 1280) 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.