Robust Pre-Attentive Attention Direction Using Chaos Theory for Video Surveillance

Author(s)
Michael E. Farmer

Affiliation(s)

Department of Computer Science, Engineering and Physics, University of Michigan-Flint, Flint, USA.

Department of Computer Science, Engineering and Physics, University of Michigan-Flint, Flint, USA.

ABSTRACT

Attention direction for active vision systems has been of substantial interest in the image processing and computer vision communities for video surveillance. Biological vision systems have been shown to possess a hierarchical structure where a pre-attentive processing function directs the visual attention to regions of interest which are then possibly further processed by higher-level vision functions. Biological neural systems are also highly responsive to signals which appear to be chaotic in nature. In this paper we explore applying measures from chaos theory and fractal analysis to provide a robust pre-attentive processing engine for vision. The approach is applied to two standard data sets related to video surveillance for detecting bags left suspiciously in public places. Results compare quite favorably in terms of probability of detection versus false detection rate shown through Receiver Operating Characteristic (ROC) curves against two traditional methods for low-level change detection, namely Mutual Information, Sum of Absolute Differences, and Gaussian Mixture Models.

Cite this paper

M. Farmer, "Robust Pre-Attentive Attention Direction Using Chaos Theory for Video Surveillance,"*Applied Mathematics*, Vol. 4 No. 9, 2013, pp. 43-45. doi: 10.4236/am.2013.49A007.

M. Farmer, "Robust Pre-Attentive Attention Direction Using Chaos Theory for Video Surveillance,"

References

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http://dx.doi.org/10.1016/j.image.2004.12.001

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[23] H. O. Peitgen, H. Jurgens and D. Saupe, “Chaos and Fractals,” Springer, Berlin, 1992.

http://dx.doi.org/10.1007/978-1-4757-4740-9

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[25] J. Theiler, “Estimating Fractal Dimension,” Journal Optical Society of America, Vol. 7, No. 6, 1990, pp. 10551073. http://dx.doi.org/10.1364/JOSAA.7.001055

[26] D. Russakoff, C. Tomasi, T. Rohlfing and C. R. Maurer, “Image Similarity Using Mutual Information of Regions,” Proceedings of the 8th European Conf. on Computer Vision, 2004, pp. 596-607.

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http://dx.doi.org/10.1016/j.patrec.2005.11.005

[28] CAVIAR Project/IST 2001 37540. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

[29] I-Lids Dataset for AVSS 2007.

[30] C. Tricot, “Curves and Fractal Dimension,” SpringerVerlag, Berlin, 1995. http://dx.doi.org/10.1007/978-1-4612-4170-6

[1] J. M. Wolfe and T. S. Horowitz, “What Attributes Guide the Deployment of Visual Attention and How Do They Do It?” Neuroscience, Vol. 5, 2004, pp. 1-7.

[2] O. LeMeur, P. LeCallet, D. Barba and D, Thoreau, “A Coherent Computational Approach to Model Bottom-Up Visual Attention,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 5, 2006, pp. 802-817. http://dx.doi.org/10.1109/TPAMI.2006.86

[3] J. M. Wolfe, “Visual Attention,” In: K. K. DeValois, Ed., Seeing, 2nd Edition, Academic Press, Amsterdam, 2000, pp. 335-386. http://dx.doi.org/10.1016/B978-012443760-9/50010-6

[4] R. J. Radke, S. Andra, O. Al-Kofahi and B. Roysam, “Image Change Detection Algorithms: A Systematic Survey,” IEEE Transactions on Image Processing, Vol. 14, No. 3, 2005, pp. 294-307.

http://dx.doi.org/10.1109/TIP.2004.838698

[5] M. J. Black, D. J. Fleet and Y. Yacoob, “Robustly Estimating Changes in Image Appearance,” Computer Vision and Image Understanding, Vol. 78, 2000, pp. 8-31. http://dx.doi.org/10.1006/cviu.1999.0825

[6] R. Mester, T. Aach and L. Dumbgen, “Illumination-Invariant Change Detection Using a Statistical Colinearity Criterion,” Proceedings of the 23rd Symposium DAGM on Pattern Recognition, 2001, pp. 170-177.

[7] M. C. Park, K. J. Cheoi and T. Hamamoto, “A Smart Image Sensor with Attention Module,” Proceedings of the 7th IEEE Workshop on Computer Architecture for Machine Perception, 2005, pp 46-51. http://dx.doi.org/10.1109/CAMP.2005.7

[8] Y. Tian, R. S. Feris, H. Liu, A. Hamparar and M.-T. Sun, “Robust Detection of Abandoned and Removed Objects in Complex Surveillance Videos,” IEEE Transactions on Systems, Man, and Cybernetics-Part C: Applications and Reviews, future issue, 2010.

[9] F. Wang, Y. Wu, Q. Zhang, P. Zhang, M. Li and Y. Lu, “Unsupervised Change Detection on SAR Images Using Triplet Markov Field Model,” IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 4, 2013, pp. 697-701. http://dx.doi.org/10.1109/LGRS.2012.2219494

[10] L. Bruzzone and F. Bovolo, “A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images,” Proceedings of the IEEE, Vol. 101, No. 3, 2013, pp. 609-630. http://dx.doi.org/10.1109/JPROC.2012.2197169

[11] Y. Pu, W. Wang and Q. Xu, “Image Change Detection Based on the Minimum Mean Square Error,” Proceedings of IEEE 5th International Joint Conference on Computational Sciences and Optimization, 2012, pp. 367-371.

[12] Y. Tian, Y. Wang, Z. Hu and T. Huang, “Selective Eigen Background for Background Modeling and Subtraction in Crowded Scenes,” IEEE Transactions on Circuits and Systems for Video Technology, preprint, 2013.

[13] M. Farmer, “A Chaos Theoretic Analysis of Motion and Illumination in Video Sequences,” Journal of Multimedia, Vol. 2, No. 2, 2007, pp. 53-64. http://dx.doi.org/10.4304/jmm.2.2.53-64

[14] M. Farmer and C. Yuan, “An Algorithm for Motion and Change Detection in Image Sequences Based on Chaos and Information Theory,” Proceedings of the SPIE, Vol. 6812, 2008, pp. 68120K-68120K-12. http://dx.doi.org/10.1117/12.766934

[15] M. E. Farmer, “A Comparison of a Chaos-Theoretic Method for Pre-Attentive Vision with Traditional GrayscaleBased Methods,” Proceedings of the IEEE Conference on Advanced Video and Signal-Based Surveillance, 2011, pp. 337-342.

[16] J. L. P. Velaquez, “Brain, Behaviour, and Mathematics: Are We Using the Right Approaches?” Physica D, No. 212, 2005, pp. 161-182. http://dx.doi.org/10.1016/j.physd.2005.10.005

[17] J.-H. Cho and S.-D. Kim, “Object Detection Using MultiResolution Mosaic in Image Sequences,” Signal Processing: Image Communication, Vol. 20, 2005, pp. 233-253.

http://dx.doi.org/10.1016/j.image.2004.12.001

[18] I. Sato, Y. Sato and K. Ikeuchi, “Illumination from Shadows,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 3, 2003, pp. 290-300.

http://dx.doi.org/10.1109/TPAMI.2003.1182093

[19] J. Bang, D. Kim and H. Eom, “Motion Object and Regional Detection Method Using Block-based Background Difference Video Frames,” Proceedings of the IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, 2012, pp. 350-357.

[20] N. Nagao, H. Nishimura and N. Matsui, “A Neural Chaos Model of Multistable Perception,” Neural Processing Letters, Vol. 12, No. 3, 2000, pp. 267-276. http://dx.doi.org/10.1023/A:1026511124944

[21] T. Tel and M. Gruiz, “Chaotic Dynamics,” Cambridge, 2006.

[22] W. Kinsner, “A Unified Approach to Fractal Dimensions,” Proceedings of the IEEE Conference on Cognitive Informatics, 2005, pp. 58-72.

[23] H. O. Peitgen, H. Jurgens and D. Saupe, “Chaos and Fractals,” Springer, Berlin, 1992.

http://dx.doi.org/10.1007/978-1-4757-4740-9

[24] A. J. Roberts, “Use the Information Dimension, Not the Hausdorff,” Nonlinear Sciences, 2005, pp. 1-9.

[25] J. Theiler, “Estimating Fractal Dimension,” Journal Optical Society of America, Vol. 7, No. 6, 1990, pp. 10551073. http://dx.doi.org/10.1364/JOSAA.7.001055

[26] D. Russakoff, C. Tomasi, T. Rohlfing and C. R. Maurer, “Image Similarity Using Mutual Information of Regions,” Proceedings of the 8th European Conf. on Computer Vision, 2004, pp. 596-607.

[27] Z. Zivkovic and F. van der Heijden, “Efficient Adaptive Density Estimation per Image Pixel for the Task of Background Subtraction,” Pattern Recognition Letters, Vol. 27, No. 7, 2006, pp. 773-780.

http://dx.doi.org/10.1016/j.patrec.2005.11.005

[28] CAVIAR Project/IST 2001 37540. http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

[29] I-Lids Dataset for AVSS 2007.

[30] C. Tricot, “Curves and Fractal Dimension,” SpringerVerlag, Berlin, 1995. http://dx.doi.org/10.1007/978-1-4612-4170-6