Abstract
Semantic road labeling is a key component of systems that aim at assisted or even autonomous driving. Considering that such systems continuously operate in the real-world, unforeseen conditions not represented in any conceivable training procedure are likely to occur on a regular basis. In order to equip systems with the ability to cope with such situations, we would like to enable adaptation to such new situations and conditions at runtime. We study the effect of changing test conditions on scene labeling methods based on a new diverse street scene dataset. We propose a novel approach that can operate in such conditions and is based on a sequential Bayesian model update in order to robustly integrate the arriving images into the adapting procedure.
Recommended for submission to YRF2014 by Dr. Mario Fritz.
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References
Alvarez, J.M., Gevers, T., LeCun, Y., Lopez, A.M.: Road scene segmentation from a single image. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VII. LNCS, vol. 7578, pp. 376–389. Springer, Heidelberg (2012)
Álvarez, J.M., López, A.M.: Road detection based on illuminant invariance. IEEE Trans. on ITS 12(1), 184–193 (2011)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Dellaert, F., Burgard, W., Fox, D., Thrun, S.: Using the condensation algorithm for robust, vision-based mobile robot localization. In: CVPR (1999)
Isard, M., Blake, A.: Condensation - conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998)
Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with gaussian edge potentials. In: NIPS (2011)
Kulis, B., Saenko, K., Darrell, T.: What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: CVPR (2011)
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 213–226. Springer, Heidelberg (2010)
Wojek, C., Schiele, B.: A dynamic conditional random field model for joint labeling of object and scene classes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 733–747. Springer, Heidelberg (2008)
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Levinkov, E. (2014). Scene Segmentation in Adverse Vision Conditions. In: Jiang, X., Hornegger, J., Koch, R. (eds) Pattern Recognition. GCPR 2014. Lecture Notes in Computer Science(), vol 8753. Springer, Cham. https://doi.org/10.1007/978-3-319-11752-2_64
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DOI: https://doi.org/10.1007/978-3-319-11752-2_64
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