Weather Condition Recognition Based on Feature Extraction and K-NN
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.
KeywordsWeather condition recognition Image inflection point Power spectral slope Edge gradient energy Noise estimation
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).
- 1.Roser M, Moosmann F (2008) Classification of weather situations on single color images. In: 2008 IEEE intelligent vehicles symposium, IEEE Press, pp 798–803 Google Scholar
- 2.Garg K, Nayar SK (2007) Vision and rain. Int J Comput Vis 75(1):3–27Google Scholar
- 3.Narasimhan SG, Nayar SK (2000) Chromatic framework for vision in bad weather. In: IEEE conference on computer vision and pattern recognition, vol 1, pp 598–605Google Scholar
- 4.Kurihata H, Takahashi T, Ide I et al (2005) Rainy weather recognition from in-vehicle camera images for driver assistance. In: Proceedings of the IEEE intelligent vehicles symposium, IEEE Press, pp 205–210Google Scholar
- 5.Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories, computer vision and pattern recognition. In: 2006 IEEE computer society conference on computer vision and pattern recognition, IEEE Press, vol 2, pp 2169–2178Google Scholar
- 6.Fergus R, Fei–Fei L, Perona P, Zisserman A (2005) Learning object categories from google’s image search. In: Proceedings of the tenth IEEE international conference on computer vision, IEEE Computer Society, pp 1816–1823Google Scholar
- 7.Lowe DG (1999) Object recognition from local scale-invariant features. Proceedings of the international conference on computer vision, Corfu, pp 1150–1157Google Scholar
- 8.Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: International conference on computer vision and pattern recognition, IEEE Computer Society Press, vol 2, pp 886–893Google Scholar
- 9.Belongie S, Malik J, Puzicha J (2000) Shape context: a new descriptor for shape matching and object recognition. Adv Neural Inf Process Syst 831–837 Google Scholar
- 10.Liu R, Li Z, Jia J (2008) Image partial blur detection and classification. In: IEEE conference on computer vision and pattern recognition, IEEE Press, p 8Google Scholar
- 11.Field D (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am A 4(12):2379–2394Google Scholar
- 12.Li Q, Fam Y, Zhang J, Li BQ (2011) Method of weather recognition based on decision-tree-based SVM. J Comput Appl 31(6):1624–1627Google Scholar