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Mobile Information Systems - Volume 2017 2017, Article ID 3175186, 11 pages - https:-doi.org-10.1155-2017-3175186

Research ArticleCollege of Computer, National University of Defense Technology, Changsha, China

Correspondence should be addressed to Yiliu Feng

Received 13 February 2017; Accepted 22 March 2017; Published 26 April 2017

Academic Editor: Zhengguo Sheng

Copyright © 2017 Yiliu Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper aims at generating high-quality object proposals for object detection in autonomous driving. Most existing proposal generation methods are designed for the general object detection, which may not perform well in a particular scene. We propose several geometrical features suited for autonomous driving and integrate them into state-of-the-art general proposal generation methods. In particular, we formulate the integration as a feature fusion problem by fusing the geometrical features with existing proposal generation methods in a Bayesian framework. Experiments on the challenging KITTI benchmark demonstrate that our approach improves the existing methods significantly. Combined with a convolutional neural net detector, our approach achieves state-of-the-art performance on all three KITTI object classes.





Autor: Yiliu Feng, Wanzeng Cai, Xiaolong Liu, Huini Fu, Yafei Liu, and Hengzhu Liu

Fuente: https://www.hindawi.com/



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