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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (2): 67-73.doi: 10.6040/j.issn.1672-3961.0.2018.155

• 机器学习与数据挖掘 • 上一篇    下一篇

飞行蝙蝠标记自动提取与追踪算法

杨煦1a(),陈辉1,a*(),林游思2,屠长河1b   

  1. 1a. 山东大学信息科学与工程学院, 山东 青岛 266237
    1b. 山东大学计算机科学与技术学院, 山东 青岛 266237
    2. 弗吉尼亚理工大学电子计算与工程学院, 弗吉尼亚州 黑堡, 24060
  • 收稿日期:2018-04-18 出版日期:2019-04-20 发布日期:2019-04-19
  • 通讯作者: 陈辉 E-mail:yxpeiyang@gmail.com;huichen@sdu.edu.cn
  • 作者简介:杨煦(1991—)，男，河南商丘人，硕士，研究方向为计算机视觉. Email: yxpeiyang@gmail.com
  • 基金资助:
    国家自然科学基金重点项目(61332015);国家自然科学基金重点项目(11574183);山东省自然科学基金项目(ZR2017MF057)

Automatic landmarks identification and tracking of bat flight

Xu YANG1a(),Hui CHEN1,a*(),Yousi LIN2,Changhe TU1b   

  1. 1a. School of Information Science and Engineering, Shandong University, Qingdao 266237, Shandong, China
    1b. School of Computer Science and Technology, Shandong University, Qingdao 266237, Shandong, China
    2. Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg 24060, USA
  • Received:2018-04-18 Online:2019-04-20 Published:2019-04-19
  • Contact: Hui CHEN E-mail:yxpeiyang@gmail.com;huichen@sdu.edu.cn
  • Supported by:
    国家自然科学基金重点项目(61332015);国家自然科学基金重点项目(11574183);山东省自然科学基金项目(ZR2017MF057)

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摘要：

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。

关键词: 飞行蝙蝠, 特征提取, 伪装, 标记追踪, 点集本准

Abstract:

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.

Key words: flight bat, feature identification, camouflage, landmark tracking, points registration

中图分类号: 

  • TP391

图1

飞行蝙蝠标记提取算法流程图"

图2

ViBe算法(a)与本研究算法(b)效果对比"

图3

平方运算前后背景强度直方图对比"

图4

蝙蝠标记分布图及区域划分"

图5

飞行通道示意图"

图6

SIFT, BRISK和本研究算法的标记提取效果对比"

表1

SIFT, BRISK,光流法与本研究算法性能对比"

算法 P R ACC 匹配准确率
SIFT 0.30 0.61 0.45 0.15
BRISK 0.54 0.98 0.76 0.20
光流法 - - - 0.55
本研究算法 0.98 0.94 0.96 0.93

图7

SIFT,光流和本研究算法的效果对比"

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