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Weather Condition Recognition Based on Feature Extraction and K-NN

  • Hongjun Song
  • Yangzhou Chen
  • Yuanyuan Gao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

Most of vision based transport parameter detection algorithms are designed to be executed in good-natured weather conditions. However, limited visibility in rain or fog strongly influences detection results. To improve machine vision in adverse weather situations, a reliable weather conditions detection system is necessary as a ground base. In this article, a novel algorithm for weather condition automatic recognition is presented. This proposed system is able to distinguish between multiple weather situations based on the classification of single monocular color images without any additional assumptions or prior knowledge. Homogenous area is extracted form top to bottom in scene image. Inflection point information which implies visibility distance will be taken as a character feature for current weather recognition. Another four features: power spectral slope, edge gradient energy, contrast, saturation, and image noisy which descript image definition are extracted also. Our proposed image descriptor clearly outperforms existing descriptors for the task. Experimental results on real traffic images are characterized by high accuracy, efficiency, and versatility with respect to driver assistance systems.

Keywords

Weather condition recognition Image inflection point Power spectral slope Edge gradient energy Noise estimation 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61079001), China’s 863 Program (No. 2011AA110301), and China’s PH.D Program Foundation (No. 20111103110017).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Institute of Autonomous Technology and Intelligent ControlBeijing University of TechnologyBeijingChina
  2. 2.College of Information and EngineeringZhejiang A&F UniversityHangzhouChina

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