飞行蝙蝠标记自动提取与追踪算法

Automatic landmarks identification and tracking of bat flight

蝙蝠对仿生扑翼飞行器研究具有重要启发价值。通过计算机视觉方法分析蝙蝠运动需要大量特征标记,因此准确提取、追踪标记是蝙蝠飞行研究的关键。常用的底层特征提取方法将局部极值作为特征点容易导致较高的标记错检率。提出一种基于图像分割的标记提取方法。通过帧间差分获取初始蝙蝠区域,对伪装区域进行补偿,利用LoG算子进行标记增强,并通过阈值分割得到标记,计算标记质心作为特征点。提出一种基于迭代最近点的标记追踪方法,将蝙蝠划分为不同区域并对区域内标记点进行点集粗配准,通过最近邻搜索完成匹配。试验结果表明,算法的标记识别率能够达到96%并实现无遮挡情况的标记追踪,优于SIFT、BRISK等特征匹配方法以及光流追踪方法。

Bats could serve as an inspiration for flapping-wing air vehicles. Understanding bats flight with computer vision techniques required a large copious of fiducial landmarks. Thus, accuracy of landmark identification and tracking was critical to bat flight research. General low-level feature extraction methods based on local extrema often resulted in high false positives. A landmark identification method based on image segmentation was proposed. An initial bat silhouette was first obtained using frame difference and then refined by compensating camouflage parts. The landmarks were enhanced by LoG operation. Finally, the coordinates of landmarks were computed from the centroids of connected components. Furthermore, a landmark tracking method based on ICP(Iterative Closet Points) was proposed. Bat region was divided into several parts, in which landmarks were aligned by ICP. The correspondences were determined by the nearest neighbor search. The method reached an identification accuracy up to 96%, and could track the landmark correctly when occlusion wasn’ occurred, which was better than SIFT, BRISK, and optical flow tracking methods.