文章基本信息

标题：Improved Object Proposals with Geometrical Features for Autonomous Driving
本地全文：下载
作者：Yiliu Feng ; Wanzeng Cai ; Xiaolong Liu 等
期刊名称：Mobile Information Systems
印刷版ISSN：1574-017X
出版年度：2017
卷号：2017
DOI：10.1155/2017/3175186
出版社：Hindawi Publishing Corporation
摘要：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.