access icon free Fog level estimation using non-parametric intensity curves in road environments

A fog level estimation method using intensity curves arranged with geometrical information is proposed. The curves, extracted from pixels in the road region and reflecting the tendency of intensity convergence under foggy conditions, are provided to the stacked auto-encoders and the encoded features are classified into four fog levels by a neural network. Experimental results with clear and foggy road images of various places show that the intensity curve feature of the proposed method is effectively working not only in detecting the presence of fog, but also in estimating the fog level accurately.

Inspec keywords: fog; feature extraction; image resolution

Other keywords: feature extraction; nonparametric intensity curves; fog level estimation method; geometrical information; image pixels; stacked auto-encoders; neural network

Subjects: Computer vision and image processing techniques; Image recognition

References

    1. 1)
      • 3. Negru, M., Nedevschi, S.: ‘Image based fog detection and visibility estimation for driving assistance systems’. 2013 IEEE 9th Int. Conf. Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania, September 2013, pp. 163168.
    2. 2)
      • 7. Danielsson, P.-E., Seger, O.: ‘Generalized and separable Sobel operators’. inFreeman, H. (ED.): ‘Machine vision for three-dimensional scenes, (Academic press, Piscataway, NJ, USA, 1990), pp. 347379.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 1. Liu, C., Lu, X., Ji, S., et al: ‘A fog level detection method based on image hsv color histogram’. 2014 IEEE Int. Conf. Progress in Informatics and Computing, Shanghai, China, May, 2014, pp. 373377.
    8. 8)
      • 13. Middleton, W.E.K.: ‘Vision through the atmosphere’, in Bartels, J. (ED.): Geophysik II/Geophysics II (Springer Berlin Heidelberg, Berlin, Heidelberg, 1957), pp. 254287.
    9. 9)
    10. 10)
      • 6. Wren, C., Azarbayejani, A., Darrell, T., et al: ‘Pfinder: real-time tracking of the human body’. Proc. of the Second Int. Conf. Automatic Face and Gesture Recognition, Killington, VT, USA, October 1996, pp. 5156.
    11. 11)
    12. 12)
      • 2. Pavlic, M., Rigoll, G., Ilic, S.: ‘Classification of images in fog and fog-free scenes for use in vehicles’. 2013 IEEE Intelligent Vehicles Symp. (IV), Gold Coast, Australia, June 2013, pp. 481486.
    13. 13)
      • 4. Ng, A.Y.: ‘Sparse autoencoder’. tech. rep., Stanford University, 2011.
http://iet.metastore.ingenta.com/content/journals/10.1049/el.2017.1753
Loading

Related content

content/journals/10.1049/el.2017.1753
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading