Abstract
Dynamic textures can be considered to be spatio-temporally varying visual patterns in image sequences with certain temporal regularity. We propose a novel and efficient approach to explore the violation of the brightness constancy assumption, as an indication of presence of dynamic texture, using simple optical flow techniques. We assume that dynamic texture regions are those that have poor spatio-temporal optical flow coherence. Further, we propose a second approach that uses robust global parametric motion estimators that effectively and efficiently detect motion outliers, and which we exploit as powerful cues to localize dynamic textures. Experimental and comparative studies on a range of synthetic and real-world dynamic texture sequences show the feasibility of the proposed approaches, with results which are competitive to or better than recent state-of-art approaches and significantly faster.
Chapter PDF
Similar content being viewed by others
Keywords
- Local Binary Pattern
- Dynamic Texture
- Smoke Detection
- Background Oriented Schlieren
- Global Parametric Motion
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Chetverikov, D., Péteri, R.: A brief survey of dynamic texture description and recognition. In: Proc. Intl. Conf. Computer Recognition Systems, pp. 17–26 (2005)
Péteri, R., Chetverikov, D.: Dynamic texture recognition using normal flow and texture regularity. In: IbPRIA, pp. 223–230 (2005)
Doretto, G., Chiuso, A., Wu, Y.N., Soatto, S.: Dynamic textures. IJCV 51, 91–109 (2003)
Atcheson, B., Heidrich, W., Ihrke, I.: An evaluation of optical flow algorithms for background oriented schlieren imaging. Experiments in Fluids 46, 467–476 (2009)
Vezzani, R., Calderara, S., Piccinini, P., Cucchiara, R.: Smoke detection in video surveillance: The use of ViSOR. In: ACM IVR, pp. 289–297 (2008)
Zhao, G., Pietikäinen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE-PAMI 29, 915–928 (2007)
Saisan, P., Doretto, G., Wu, Y.N., Soatto, S.: Dynamic texture recognition. In: CVPR, vol. 2, pp. 58–63 (2001)
Hyndman, M., Jepson, A., Fleet, D.: Higher-order autoregressive models for dynamic textures. In: BMVC (2007)
Chan, A.B., Vasconcelos, N.: Variational layered dynamic textures. In: CVPR (2009)
Vidal, R., Ravichandran, A.: Optical flow estimation and segmentation of multiple moving dynamic textures. In: CVPR, pp. 516–521 (2005)
Chan, A., Vasconcelos, N.: Layered dynamic textures. IEEE-PAMI 31, 1862–1879 (2009)
Doretto, G., Cremers, D., Favaro, P., Soatto, S.: Dynamic texture segmentation. In: ICCV, vol. 2, pp. 1236–1242 (2003)
Campisi, P., Maiorana, E., Neri, A., Scarano, G.: Video texture modelling and synthesis using fractal processes. IET Image Processing 2, 1–17 (2008)
Lu, Z., Xie, W., Pei, J., Huang, J.: Dynamic texture recognition by spatiotemporal multiresolution histograms. In: IEEE Workshop. on Motion & Video Computing, vol. 2, pp. 241–246 (2005)
Ghanem, B., Ahuja, N.: Extracting a fluid dynamic texture and the background from video. In: CVPR (2008)
Toreyin, B., Cetin, A.: HMM based method for dynamic texture detection. In: IEEE 15th. Signal Processing and Communications Applications (2007)
Ferrari, R.J., Zhang, H., Kube, C.R.: Real-time detection of steam in video images. PR 40, 1148–1159 (2007)
Xiong, X., Caballero, R., Wang, H., Finn, A.M., Lelic, M.A., Peng, P.Y.: Video-based smoke detection: Possibilities, techniques, and challenges. In: IFPA (2007)
Toreyin, B., Cetin, A.: On-line detection of fire in video. In: CVPR (2007)
Corpetti, T., Memin, E., Pérez, P.: Dense estimation of fluid flows. IEEE-PAMI 24, 365–380 (2002)
Toreyin, B.U., Dedeoglu, Y., Cetin, A.E.: Wavelet based real-time smoke detection in video. In: EUSIPCO (2005)
Rahman, A., Murshed, M.: Real-time temporal texture characterisation using block based motion co-occurrence statistics. In: ICIP, pp. III: 1593–1596 (2004)
Bouthemy, P., Hardouin, C., Piriou, G., Yao, J.: Mixed-state auto-models and motion texture modeling. JMIV 25, 387–402 (2006)
Fazekas, S., Chetverikov, D.: Analysis and performance evaluation of optical flow features for dynamic texture recognition. Signal Processing: Image Comm. 22, 680–691 (2007)
Fazekas, S., Amiaz, T., Chetverikov, D., Kiryati, N.: Dynamic texture detection based on motion analysis. IJCV 82, 48–63 (2009)
Viola, P.A., Jones, M.J.: Robust real-time face detection. IJCV 57, 137–154 (2004)
Odobez, J., Bouthemy, P.: Robust multiresolution estimation of parametric motion models. Int. J. Visual Communication and Image Representation 6, 348–365 (1995)
Chetverikov, D., Fazekas, S., Haindl, M.: Dynamic texture as foreground and background. In: MVA (2010), doi:10.1007/s00138-010-0251-6 (Published online: February 21, 2010)
Fazekas, S., Amiaz, T., Chetverikov, D., Kiryati, N.: (Dynamic texture detection and segmenation), http://vision.sztaki.hu/~fazekas/dtsegm
Péteri, R., Huskies, M., Fazekas, S. (DynTex: a comprehensive database of dynamic textures), http://www.cwi.nl/projects/dyntex
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
1 Electronic Supplementary Material
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Traver, V.J., Mirmehdi, M., Xie, X., Montoliu, R. (2010). Fast Dynamic Texture Detection. In: Daniilidis, K., Maragos, P., Paragios, N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science, vol 6314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15561-1_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-15561-1_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15560-4
Online ISBN: 978-3-642-15561-1
eBook Packages: Computer ScienceComputer Science (R0)