Weather Condition Recognition Based on Feature Extraction and K-NN

摘要

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.