Abstract
A framework for the automatic detection of dangerously deteriorating visibility (e.g. due to bad weather and/or poor illumination conditions) is presented. The method employs image matching techniques for tracking similar fragments in video-frames captured by a forward-looking camera. The visibility is considered low when performances of visual tracking deteriorate and/or its continuity is lost either temporarily (i.e. a sudden burst of light, a splash of water) or more permanently. Two variants of the tracking algorithm are considered, i.e. the topological approach (more important) and the geometric one. Using the most difficult examples of DAGM2011 Challenge dataset (e.g. Snow, Rain and Light-sabre clips) it is demonstrated that the visibility quality can be numerically estimated, and the most severe cases (when even the human eye can hardly recognize the scene components) are represented by zero (or near-zero) values. The paper also briefly discusses the implementation issues (based on a previously developed similar real-time application) and directions of future works.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Apostoloff, N., Zelinsky, A.: Robust vision-based lane tracking using multiple cues and particle filtering. In: Proc. of IEEE Symp. on Intelligent Vehicles, pp. 558–563 (2003)
McCall, J.C., Trivedi, M.M.: Video-based lane estimation and tracking for driver assistance: Survey, system, and evaluation. IEEE Transactions on Intelligent Transportation Systems 7, 20–37 (2006)
Betke, M., Haritaoglu, E., Davis, L.S.: Real-time multiple vehicle detection and tracking from a moving vehicle. Machine Vision and Applications 12, 69–83 (2000)
Fossati, A., Schonmann, P., Fua, P.: Real-time vehicle tracking for driving assistance. Machine Vision and Applications 22, 439–448 (2011)
de la Escalera, A., Armingol, J.M., Mata, M.: Traffic sign recognition and analysis for intelligent vehicles. Image and Vision Computing 21, 247–258 (2003)
Broggi, A., Cerri, P., Medici, P., Porta, P.P., Ghisio, G.: Real time road signs recognition. In: Proc. of IEEE Symp. on Intelligent Vehicles, pp. 981–986 (2007)
Mikolajczyk, K., Schmid, C.: Scale and affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Paradowski, M., Śluzek, A.: Local Keypoints and Global Affine Geometry: Triangles and Ellipses for Image Fragment Matching. In: Kwaśnicka, H., Jain, L.C. (eds.) Innovations in Intelligent Image Analysis. SCI, vol. 339, pp. 195–224. Springer, Heidelberg (2011)
Paradowski, M., Śluzek, A.: Keypoint-based detection of near-duplicate image fragments using image geometry and topology. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010. LNCS, vol. 6375, pp. 175–182. Springer, Heidelberg (2010)
Śluzek, A., Paradowski, M.: Real-time retrieval of near-duplicate fragments in images and video-clips. In: Blanc-Talon, J., Bone, D., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2010, Part I. LNCS, vol. 6474, pp. 18–29. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Śluzek, A., Paradowski, M. (2011). Assessment of Visibility Quality in Adverse Weather and Illumination Conditions. In: Mester, R., Felsberg, M. (eds) Pattern Recognition. DAGM 2011. Lecture Notes in Computer Science, vol 6835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23123-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-23123-0_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23122-3
Online ISBN: 978-3-642-23123-0
eBook Packages: Computer ScienceComputer Science (R0)