Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (2): 67-73.doi: 10.6040/j.issn.1672-3961.0.2018.155

• Machine Learning & Data Mining • Previous Articles     Next Articles

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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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

CLC Number: 

  • TP391

Fig.1

Flowchart of landmark identification algorithm"

Fig.2

Comparison between ViBe(a) and the proposed method(b)"

Fig.3

Comparison of intensity of image before and after square operation"

Fig.4

Landmark layouts and wing region partitions"

Fig.5

Flight tunnel diagram"

Fig.6

Comparison of SIFT, BRISK and the proposed algorithm on landmark identification"

Table 1

Comparison of SIFT, BRISK, Optical flowand our method"

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

Fig.7

Comparison among SIFT, optical flow andthe proposed method"

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