A HUMAN ACTION CLASSIFIER FROM 4-D DATA (3-D+TIME) - Based on an Invariant Body Shape Descriptor and Hidden Markov Models

Massimiliano Pierobon, Marco Marcon, Augusto Sarti, Stefano Tubaro

Abstract

Many human action definitions have been provided in the field of human computer interaction studies. These distinctions could be considered merely semantical as human actions are all carried out performing sequences of body postures. In this paper we propose a human action classifier based on volumetric reconstructed sequences (4-D data) acquired from a multi-viewpoint camera system. In order to design the most general action classifier possible, we concentrate our attention in extracting only posture-dependent information from volumetric frames and in performing action distinction only on the basis of the sequence of body postures carried out in the scene. An Invariant Shape Descriptor (ISD) is used in order to properly describe the body shape and its dynamic changes during an action execution. The ISD data is then analyzed in order to extract suitable features able to meaningfully represent a human action independently from body position, orientation, size and proportions. The action classification is performed using a supervised recognizer based on the Hidden Markov Models (HMM) theory. Experimental results, evaluated using an extensive action sequence dataset and applying different training conditions to the HMM-based classifier, confirm the reliability of the proposed approach.

References

  1. Aggarwal, J. K. and Cai, Q. (1997). Human motion analysis: A review. In IEEE Proceedings of Nonrigid and Articulated Motion Workshop.
  2. Cohen, I. and Li, H. (2003). Inference of human postures by classification of 3d human body shape. In IEEE Proceedings of International Workshop on Analysis and Modeling of Faces and Gestures.
  3. Collins, R., Lipton, A., and Kanade, T. (2000). Introduction to the special section on video surveillance. In IEEE Transactions on Pattern Analysis and Machine Intelligence.
  4. Cui, Y. and Weng, J. (1996). Hand segmentation using learning-based prediction and verification for hand sign recognition. In Proceedings of IEEE CS Conference on Computer Vision and Pattern Recognition.
  5. Cunado, D., Nixon, M., and Carter, J. (1998). Automatic gait recognition via model-based evidence gathering. In Proceedings of Workshop on Automatic Identification Advanced Technologies.
  6. Cuzzolin, F., Sarti, A., and Tubaro, S. (2004). Invariant action classification with volumetric data. In IEEE Proceedings of Workshop on Multimedia Signal Processing.
  7. Freeman, W., Tanaka, K., Ohta, J., and Kyuma, K. (1996). Computer vision for computer games. In Proceedings of International Conference on Automatic Face and Gesture Recognition.
  8. Gavrila, D. (1999). The visual analysis of human movement: A survey. In Computer Vision and Image Understanding, vol.73, no.1. Academic Press.
  9. Geer, D. (2004). Will gesture technology point the way? In Computer.
  10. Huang, P., Harris, C., and Nixon, M. (1999). Human gait recognition in canonical space using temporal templates. In Proceedings of IEEE Vision Image Signal Processing.
  11. Ivanov, Y., Stauffer, C., Bobick, A., and Grimson, W. E. L. (1998). Video surveillance of interactions. In IEEE Proceedings of the CVPR'99 Workshop on Visual Surveillance.
  12. Köhle, M., Merkl, D., and Kastner, J. (1997). Clinical gait analysis by neural networks: issues and experiences. In Proceedings of IEEE Symposium on ComputerBased Medical Systems.
  13. Lakany, H., Haycs, G., Hazlewood, M., and Hillman, S. (1999). Human walking: tracking and analysis. In Proceedings of IEE Colloquium on Motion Analysis and Tracking.
  14. Laurentini, A. (1994). The visual hull concept for silhouette-based image understanding. In IEEE Transactions on Pattern Analysis and Machine Intelligence.
  15. Li, Y., Ma, S., and Lu, H. (1998). Human posture recognition using multi-scale morphological method and kalman motion estimation. In Proceedings of IEEE International Conference on Pattern Recognition.
  16. Little, J. and Boyd, J. (1998). Recognizing people by their gait: the shape of motion. In Journal of Computer Vision Research.
  17. Maybank, S. and Tan, T. (2000). Introduction to special section on visual surveillance. In International Journal of Computer Vision.
  18. Meyer, D., Denzler, J., and Niemann, H. (1997). Model based extraction of articulated objects in image sequences for gait analysis. In Proceedings of IEEE International Conference on Image Processing.
  19. Mikic, I., Trivedi, M., Hunter, E., and Cosman, P. (2001). Articulated body posture estimation from multi-camera voxel data. In IEEE Proceedings of the Conference on Computer Vision and Pattern Recognition.
  20. Nespoulous, J.-L. and Perron, P. (1986). THE BIOLOGICAL FOUNDATIONS OF GESTURES: Motor and Semiotic Aspects. Lawrence Erlbaum Associates, Hillsdale, New Jersey London.
  21. Rabiner, L. (1989). A tutorial on hidden markov models and selected applications in speech recognition. In Proceedings of the IEEE.
  22. Segen, J. and Kumar, S. (1999). Shadow gestures: 3d hand pose estimation using a single camera. In Proceedings of IEEE CS Conference on Computer Vision and Pattern Recognition.
  23. Shutler, J., Nixon, M., and Harris, C. (2000). Statistical gait recognition via velocity moments. In Proceedings of IEEE Colloquium on Visual Biometrics.
  24. Yang, M.-H. and Ahuja, N. (1999). Recognizing hand gesture using motion trajectories. In Proceedings of IEEE CS Conference on Computer Vision and Pattern Recognition.
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Paper Citation


in Harvard Style

Pierobon M., Marcon M., Sarti A. and Tubaro S. (2007). A HUMAN ACTION CLASSIFIER FROM 4-D DATA (3-D+TIME) - Based on an Invariant Body Shape Descriptor and Hidden Markov Models . In Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007) ISBN 978-989-8111-13-5, pages 396-403. DOI: 10.5220/0002143303960403


in Bibtex Style

@conference{sigmap07,
author={Massimiliano Pierobon and Marco Marcon and Augusto Sarti and Stefano Tubaro},
title={A HUMAN ACTION CLASSIFIER FROM 4-D DATA (3-D+TIME) - Based on an Invariant Body Shape Descriptor and Hidden Markov Models},
booktitle={Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007)},
year={2007},
pages={396-403},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002143303960403},
isbn={978-989-8111-13-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2007)
TI - A HUMAN ACTION CLASSIFIER FROM 4-D DATA (3-D+TIME) - Based on an Invariant Body Shape Descriptor and Hidden Markov Models
SN - 978-989-8111-13-5
AU - Pierobon M.
AU - Marcon M.
AU - Sarti A.
AU - Tubaro S.
PY - 2007
SP - 396
EP - 403
DO - 10.5220/0002143303960403