Abstract
In this work, we describe a framework to analyze UAV videos content. A multi-class image segmentation approach is proposed considering UAV videos specific characteristics. A static image segmentation is applied on each frame. After a preprocessing step on resulting segments, a SVM classifier is used to recognize regions. A Markov model is introduced to combine the results from the previous frames in order to improve the accuracy. The framework has been designed to be as flexible as possible with an eye to allow to insert holistic information into the model.
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References
Benediktsson, J.A., Pesaresi, M., Amason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing 41(9), 1940–1949 (2003)
Boroujeni, N.S., Etemad, S.A., White, A.: Robust horizon detection using segmentation for uav applications. In: 2012 Ninth Conference on Computer and Robot Vision (CRV), pp. 346–352. IEEE (2012)
Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST)Â 2(3), 27 (2011)
Cheng, P., Zhou, G., Zheng, Z.: Detecting and counting vehicles from small low-cost uav images. In: ASPRS 2009 Annual Conference, Baltimore, vol. 3, pp. 9–13 (2009)
Comaniciu, D., Meer, P.: Mean shift analysis and applications. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 1197–1203. IEEE (1999)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Eltner, A., Mulsow, C., Maas, H.G.: Quantitative measurement of soil erosion from tls and uav data. In: ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 1(2), pp. 119–124 (2013)
Fauqueur, J., Kingsbury, N., Anderson, R.: Semantic discriminant mapping for classification and browsing of remote sensing textures and objects. In: IEEE International Conference on Image Processing, ICIP 2005, vol. 2, pp. II–846. IEEE (2005)
Fukunaga, K., Hostetler, L.: The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory 21(1), 32–40 (1975)
Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 670–677. IEEE (2009)
Gould, S., Fulton, R., Koller, D.: Decomposing a scene into geometric and semantically consistent regions. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1–8. IEEE (2009)
Gould, S., Gao, T., Koller, D.: Region-based segmentation and object detection. In: NIPS, vol. 1, p. 2 (2009)
Hodson, A., Anesio, A.M., Ng, F., Watson, R., Quirk, J., Irvine-Fynn, T., Dye, A., Clark, C., McCloy, P., Kohler, J., et al.: A glacier respires: quantifying the distribution and respiration co2 flux of cryoconite across an entire arctic supraglacial ecosystem. Journal of Geophysical Research: Biogeosciences (2005–2012), 112(G4) (2007)
Hoiem, D., Efros, A.A., Hebert, M.: Putting objects in perspective. International Journal of Computer Vision 80(1), 3–15 (2008)
Hsu, C.-W., Lin, C.-J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)
Knerr, S., Personnaz, L., Dreyfus, G.: Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Neurocomputing, pp. 41–50. Springer (1990)
Lin, C., Nevatia, R.: Building detection and description from a single intensity image. Computer vision and image understanding 72(2), 101–121 (1998)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Serra, J., Vincent, L.: An overview of morphological filtering. Circuits, Systems and Signal Processing 11(1), 47–108 (1992)
Shotton, J., Winn, J., Rother, C., Criminisi, A.: Textonboost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part 1. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)
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Lathuilière, S., Vu, H., Le, TL., Tran, TH., Hung, D.T. (2015). Semantic Regions Recognition in UAV Images Sequence. In: Nguyen, VH., Le, AC., Huynh, VN. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 326. Springer, Cham. https://doi.org/10.1007/978-3-319-11680-8_25
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DOI: https://doi.org/10.1007/978-3-319-11680-8_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11679-2
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