Road environment perception algorithm based on object semantic probabilistic model

Wei Liu; XinMei Wang; Jinwen Tian; Yong Wang

doi:10.1117/12.2204737

14 December 2015 Road environment perception algorithm based on object semantic probabilistic model

Wei Liu, XinMei Wang, Jinwen Tian, Yong Wang

Proceedings Volume 9812, MIPPR 2015: Automatic Target Recognition and Navigation; 981206 (2015) https://doi.org/10.1117/12.2204737
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China

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.

Citation Download Citation

Wei Liu, XinMei Wang, Jinwen Tian, and Yong Wang "Road environment perception algorithm based on object semantic probabilistic model", Proc. SPIE 9812, MIPPR 2015: Automatic Target Recognition and Navigation, 981206 (14 December 2015); https://doi.org/10.1117/12.2204737

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KEYWORDS

Expectation maximization algorithms

Detection and tracking algorithms

Roads

Statistical modeling

Environmental sensing

Image processing

Object recognition

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