文件名称：Learning based Symmetric Features Selection for Vehicle Detection
文件大小：1.2MB
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更新时间：2014-05-01 05:36:55
Symmetric Features Selection
Learning based Symmetric Features Selection for Vehicle Detection This paper describes a symmetric features selection strategy based on statistical learning method for detecting vehicles with a single moving camera for autonomous driving. Symmetry is a good class of feature for vehicle detection, but the areas with high symmetry and threshold for segmentation is hard to be decided. Usually, the additional supposition is added artificially, and this will decrease the robustness of algorithms. In this paper, we focus on the problem of symmetric features selection using learning method for autonomous driving environment. Global symmetry and local symmetry are defined and used to construct a cascaded structure with a one-class classifier followed by a two-class classifier.