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Proceedings Paper

An object detection and tracking system for unmanned surface vehicles
Author(s): Jian Yang; Yang Xiao; Zhiwen Fang; Naiwen Zhang; Li Wang; Tao Li
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Paper Abstract

Object detection and tracking are critical parts of unmanned surface vehicles(USV) to achieve automatic obstacle avoidance. Off-the-shelf object detection methods have achieved impressive accuracy in public datasets, though they still meet bottlenecks in practice, such as high time consumption and low detection quality. In this paper, we propose a novel system for USV, which is able to locate the object more accurately while being fast and stable simultaneously. Firstly, we employ Faster R-CNN to acquire several initial raw bounding boxes. Secondly, the image is segmented to a few superpixels. For each initial box, the superpixels inside will be grouped into a whole according to a combination strategy, and a new box is thereafter generated as the circumscribed bounding box of the final superpixel. Thirdly, we utilize KCF to track these objects after several frames, Faster-RCNN is again used to re-detect objects inside tracked boxes to prevent tracking failure as well as remove empty boxes. Finally, we utilize Faster R-CNN to detect objects in the next image, and refine object boxes by repeating the second module of our system. The experimental results demonstrate that our system is fast, robust and accurate, which can be applied to USV in practice.

Paper Details

Date Published: 5 October 2017
PDF: 8 pages
Proc. SPIE 10432, Target and Background Signatures III, 104320R (5 October 2017);
Show Author Affiliations
Jian Yang, Huazhong Univ. of Science and Technology (China)
Yang Xiao, Huazhong Univ. of Science and Technology (China)
Zhiwen Fang, Hunan Univ. of Humanities, Science and Technology (China)
Naiwen Zhang, Huazhong Univ. of Science and Technology (China)
Li Wang, Beijing Institute of Control Engineering (China)
Tao Li, Beijing Institute of Control Engineering (China)

Published in SPIE Proceedings Vol. 10432:
Target and Background Signatures III
Karin U. Stein; Ric Schleijpen, Editor(s)

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