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Proceedings Paper

Road environment perception algorithm based on object semantic probabilistic model
Author(s): Wei Liu; XinMei Wang; Jinwen Tian; Yong Wang
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Paper Abstract

This article seeks to discover the object categories’ semantic probabilistic model (OSPM) based on statistical test analysis method. We applied this model on road forward environment perception algorithm, including on-road object recognition and detection. First, the image was represented by a set composed of words (local feature regions). Then, found the probability distribution among image, local regions and object semantic category based on the new model. In training, the parameters of the object model are estimated. This is done by using expectation-maximization in a maximum likelihood setting. In recognition, this model is used to classify images by using a Bayesian manner. In detection, the posterios is calculated to detect the typical on-road objects. Experiments release the good performance on object recognition and detection in urban street background.

Paper Details

Date Published: 14 December 2015
PDF: 6 pages
Proc. SPIE 9812, MIPPR 2015: Automatic Target Recognition and Navigation, 981206 (14 December 2015); doi: 10.1117/12.2204737
Show Author Affiliations
Wei Liu, China Univ. of Geoscience (China)
XinMei Wang, China Univ. of Geoscience (China)
Jinwen Tian, China Univ. of Geoscience (China)
Yong Wang, China Univ. of Geoscience (China)

Published in SPIE Proceedings Vol. 9812:
MIPPR 2015: Automatic Target Recognition and Navigation
Nong Sang; Xinjian Chen, Editor(s)

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