Regular Articles

Obstacle regions extraction method for unmanned aerial vehicles based on space–time tensor descriptor

[+] Author Affiliations
Zhenglong Wu, Jie Li, Huan Yang

Beijing Institute of Technology, School of Mechatronic Engineering, 5 Zhongguancun South Street, Beijing 100081, China

Zhenyu Guan

Beijing Electro-Mechanical Engineering Institute, 40 Yungang North Li, Beijing 100074, China

J. Electron. Imaging. 25(5), 053029 (Oct 14, 2016). doi:10.1117/1.JEI.25.5.053029
History: Received March 7, 2016; Accepted September 20, 2016
Text Size: A A A

Abstract.  Obstacle avoidance is an important and challenging task for the autonomous flight of unmanned aerial vehicles. Obstacle regions extraction from image sequences is a critical prerequisite in obstacle avoidance. We propose an obstacle regions extraction method based on space–time tensor descriptor. In our method, first, the space–time tensor descriptor is defined and a criterion function based on the descriptor of extracting space–time interest points (STIPs) is designed. Then a self-adaptive clustering of STIPs approach is presented to locate the possible obstacle regions. Finally, an improved level set algorithm is applied with the result of clustering to extract the obstacle regions. We demonstrate the experiments of obstacle regions extraction by our method on image sequences. Sequences are captured in indoor simulative obstacle avoidance environments and outdoor real flight obstacle avoidance environments. Experimental results validate that our method can effectively complete extraction and segmentation of obstacle region with captured images. Compared with the state-of-the-art methods, our method performs well to extract the contours of obstacle regions on the whole and significantly improves segmentation speed.

Figures in this Article
© 2016 SPIE and IS&T

Citation

Zhenglong Wu ; Jie Li ; Zhenyu Guan and Huan Yang
"Obstacle regions extraction method for unmanned aerial vehicles based on space–time tensor descriptor", J. Electron. Imaging. 25(5), 053029 (Oct 14, 2016). ; http://dx.doi.org/10.1117/1.JEI.25.5.053029


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.