14 October 2016 Obstacle regions extraction method for unmanned aerial vehicles based on space–time tensor descriptor
Zhenglong Wu, Jie Li, Zhenyu Guan, Huan Yang
Author Affiliations +
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.
© 2016 SPIE and IS&T 1017-9909/2016/$25.00 © 2016 SPIE and IS&T
Zhenglong Wu, Jie Li, Zhenyu Guan, and Huan Yang "Obstacle regions extraction method for unmanned aerial vehicles based on space–time tensor descriptor," Journal of Electronic Imaging 25(5), 053029 (14 October 2016). https://doi.org/10.1117/1.JEI.25.5.053029
Published: 14 October 2016
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KEYWORDS
Image segmentation

Unmanned aerial vehicles

Feature extraction

Image processing algorithms and systems

Optical flow

Visual process modeling

3D modeling

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