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Activity Recognition Via Classification Constrained Diffusion Maps

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Advances in Visual Computing (ISVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4291))

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Abstract

Applying advanced video technology to understand human activity and intent is becoming increasingly important for video surveillance. In this paper, we perform automatic activity recognition by classification of spatial temporal features from video sequence. We propose to incorporate class labels information to find optimal heating time for dimensionality reduction using diffusion via random walks. We perform experiments on real data, and compare the proposed method with existing random walk diffusion map method and dual root minimal spanning tree diffusion method. Experimental results show that our proposed method is better.

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References

  1. Shet, V.D., Harwood, D., Davis, L.S.: VidMAP: Video Monitoring of Activity with Prolog. In: IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS), Como, Italy (September 2005)

    Google Scholar 

  2. Vu, V., Bremond, F., Thonnat, M.: Video surveillance: human behaviour representation and on-line recognition. In: ideo surveillance: human behaviour representation and on-line recognition, The Sixth International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Podere d’Ombriano, Crema, Italy (September 2002)

    Google Scholar 

  3. Ma, Y., Miller, B., Buddharaju, P., Bazakos, M.: Activity Awareness: From Predefined Events to New Pattern Discovery. In: 2006 IEEE International Conference on Computer Vision Systems, January 5-7, 2006, New York, USA (2006)

    Google Scholar 

  4. Chellappa, R., Chowdhury, A., Zhou, S.: Recognition of Humans and Their Activities Using Video. Morgan Claypool (2005)

    Google Scholar 

  5. Jin, N., Mokhtarian, F.: Human Motion Recognition based on Statistical Shape Analysis. In: Proc. IEEE International Conference on Advanced Video and Signal based Surveillance, pp. 4–9 (2005)

    Google Scholar 

  6. Zhong, H., Shi, J., Visontai, M.: Detecting Unusual Activity in Video. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR) (2004)

    Google Scholar 

  7. Porikli, F., Haga, T.: Event detection by Eigenvector Decomposition using object and feature frame. In: CVPR workshop, pp. 114–114 (2004)

    Google Scholar 

  8. Belkin, M., Niyogi, P.: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation 15(6), 1373–1396 (2003)

    Article  MATH  Google Scholar 

  9. Coifman, R.R., Lafon, S.: Diffusion Maps. Applied Computational and Harmonic Analysis (submitted to, 2004)

    Google Scholar 

  10. Davis, J., Bobick, A.: The Representation and Recognition of Action Using Temporal Templates. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, June 1997, pp. 928–934. Puerto Rico (1997)

    Google Scholar 

  11. Grikschat, S., Costa, J., Hero, A., Michel, O.: Dual rooted diffusions for clustering and classification on manifolds. In: 2006 IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Toulouse France (2006)

    Google Scholar 

  12. Masoud, O., Papanikolopoulos, N.P.: A method for human action recognition. Image and Vision Computing 21(8), 729–743 (2003)

    Article  Google Scholar 

  13. Ng, A., Jordan, M., Weiss, Y.: On spectral clustering:analysis and an algorithm. Neural Information Processing Systems 14 (2001)

    Google Scholar 

  14. Ma, Y., Yu, Q., Cohen, I.: Multiple Hypothesis Target Tracking using Merge and Split of Graphs Nodes. In: 2nd International Symposium on Visual Computing, Lake Tahoe, Nevada (November 6-8, 2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Honeywell labs, Honeywell International Inc., 3660 Technology Drive, Minneapolis, MN, 55418, USA

    Yunqian Ma

  2. Institute for Mathematics and its Applications, University of Minnesota, 400 Lind Hall, 207 Church Hill, S.E Minneapolis, MN, 55455, USA

    S. B. Damelin

  3. Artificial Intelligence, Vision and Robotics Lab, Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455, USA

    O. Masoud & N. Papanikolopoulos

Authors
  1. Yunqian Ma
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  2. S. B. Damelin
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  3. O. Masoud
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  4. N. Papanikolopoulos
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

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© 2006 Springer-Verlag Berlin Heidelberg

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Ma, Y., Damelin, S.B., Masoud, O., Papanikolopoulos, N. (2006). Activity Recognition Via Classification Constrained Diffusion Maps. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_1

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  • DOI: https://doi.org/10.1007/11919476_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48628-2

  • Online ISBN: 978-3-540-48631-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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