Motion Detection Using the Dynamic Color Texture

Color interest point detection is an important research area in the field of image processing and computer vision. In general, the use of color increases the distinctiveness of interest points. In this article we propose to extend the detection of the color interest point to the temporal aspect. For color sequences, we propose an extension version of the Color interest point detector to detect what they call "Color Space- Time Interest Points detector" (CSTIP). To increase the robustness of CSTIP features extraction, we suggest a pre-processing step which is based on a dynamic decomposition model and can decomposes the video into the dynamic color texture component and the dynamic color structure component. We compute the new Color Space Time Interest Points (CSTIP) associated to the dynamic color texture (DCT) components by using the proposed algorithm of the detection of Color Space- Time Interest Points. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

[1]  Jérôme Gilles.  Décomposition et détection de structures géométriques en imagerie , 2006 .

[2]  Benoit Huet,et al.  Analysis of vector space model and spatiotemporal segmentation for video indexing and retrieval , 2007, CIVR '07.

[3]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[4]  Stanley Osher,et al.  Color Texture Modeling and Color Image Decomposition in a Variational-PDE Approach , 2006, 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[5]  Serge J. Belongie,et al.  Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.

[6]  Luc Van Gool,et al.  An Efficient Dense and Scale-Invariant Spatio-Temporal Interest Point Detector , 2008, ECCV.

[7]  Pierre Kornprobst,et al.  Mathematical problems in image processing - partial differential equations and the calculus of variations , 2010, Applied mathematical sciences.

[8]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[9]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[10]  Ivan Laptev,et al.  On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Qiong Liu,et al.  A robust skin color based face detection algorithm , 2010, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).

[12]  Michel Ménard,et al.  Dynamic Color Texture Modeling and Color Video Decomposition Using Bounded Variation and Oscillatory Functions , 2008, ICISP.

[13]  Jean-Christophe Terrillon,et al.  Comparative Performance of Different Chrominance Spaces for Color Segmentation and Detection of Human Faces in Complex Scene Images , 1999 .

[14]  Tony F. Chan,et al.  The digital TV filter and nonlinear denoising , 2001, IEEE Trans. Image Process..

[15]  Bolei Zhou,et al.  A Phase Discrepancy Analysis of Object Motion , 2010, ACCV.

[16]  Nicu Sebe,et al.  Sparse Color Interest Points for Image Retrieval and Object Categorization , 2012, IEEE Transactions on Image Processing.

[17]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[18]  Jean-François Aujol.  Contribution à l'analyse de textures en traitement d'images par méthodes variationnelles et équations aux dérivées partielles , 2004 .

[19]  Stanley Osher,et al.  Total variation based image restoration with free local constraints , 1994, Proceedings of 1st International Conference on Image Processing.

[20]  Antonin Chambolle,et al.  Dual Norms and Image Decomposition Models , 2005, International Journal of Computer Vision.

[21]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[22]  O. Rioul,et al.  Wavelets and signal processing , 1991, IEEE Signal Processing Magazine.

[23]  Y. Meyer.  Oscillating Patterns in Some Nonlinear Evolution Equations , 2006 .

[24]  Hamid Tairi,et al.  Optical flow estimation based on the structure–texture image decomposition , 2015, Signal Image Video Process..

[25]  John R. Kender,et al.  Finding skin in color images , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.