Abstract
This work is placed in the context of computer vision and ubiquitous multimedia access. It deals with the development of an automated system for human behavior analysis and prediction using context features as a representative descriptor of human posture. In our proposed method, an action is composed of a series of features over time. Therefore, time sequential images expressing human action are transformed into a feature vector sequence. Then the feature is transformed into symbol sequence. For that purpose, we design a posture codebook, which contains representative features of each action type and define distances to measure similarity between feature vectors. The system is also able to predict next performed motion. This prediction helps to evaluate and choose current action to show.
Funded by project IMSERSO-AUTOPIA.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Shadbolt, N.: Ambient intelligence. IEEE Intelligent Systems 18(4), 2–3 (2003)
Remagnino, P., Foresti, G.L., Ellis, T.: Ambient Intelligence: A Novel Paradigm. Springer, Heidelberg (2004)
Castanedo, F., Patricio, M.A., García, J., Molina, J.M.: Extending surveillance systems capabilities using bdi cooperative sensor agents. In: VSSN ’06: Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, pp. 131–138. ACM Press, New York (2006)
Patricio, M.A., Carbó, J., Pérez, O., García, J., Molina, J.M.: Multi-agent framework in visual sensor networks. EURASIP Journal on Advances in Signal Processing (2007), doi:10.1155/2007/98639
Moeslund, T.B., Granum, E.: A survey of computer vision-based human motion capture. Computer Vision and Image Understanding: CVIU 81(3), 231–268 (2001)
Ju, S.X., Black, M.J., Yacoob, Y.: Cardboard people: A parameterized model of articulated image motion. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition (FGR’96), pp. 38–44. Dept. of Comput. Sci., Toronto Univ., Ont. (1996)
Heisele, B., Wohler, C.: Motion-based recognition of pedestrians. In: Intenational Conference of Pattern Recognition, Brisbane, Qld., Australia (1998)
Bregler, C.: Learning and recognizing human dynamics in video sequences. In: Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’97), U.C. Berkeley (1998)
Koffka, K.: Principle of Gestalt Psychology. Harcourt Brace, New York (1935)
Pérez, Ó., Piccardi, M., García, J., Patricio, M.Á., Molina, J.M.: Comparison between genetic algorithms and the baum-welch algorithm in learning hMMs for human activity classification. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 399–406. Springer, Heidelberg (2007)
Mori, G., Malik, J.: Recovering 3d human body configurations using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 28(7), 1052–1062 (2006)
Liebowitz, D., Carlsson, S.: Uncalibrated motion capture exploiting articulated structure constraints. International Journal of Computer Vision 51, 171–187 (2003)
Loy, G., Eriksson, M., Sullivan, J., Carlsson, S.: Monocular 3d reconstruction of human motion in long action sequences. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 442–455. Springer, Heidelberg (2004)
Taylor, C.J.: Reconstruction of articulated objects from point correspondences in a single uncalibrated image. Computer Vision and Image Understanding, CVIU 80(3), 349–363 (2000)
Howe, N.R., Leventon, M.E., Freeman, W.T.: Bayesian reconstruction of 3d human motion from single-camera video. In: Advances in Neural Information Processing Systems (NIPS), Denver, CO, pp. 820–826 (2000)
Navaratnam, R., Thayananthan, A., Torr, P., Cipolla, R.: Hierarchical part-based human body pose estimation. In: Proceedings of the British Machine Vision Conference (BMVC’05), Oxford, United Kingdom (2005)
Huang, Y., Huang, T.S.: Model-based human body tracking. In: Proceedings of the International Conference on Pattern Recognition (ICPR’02), Quebec, Canada, pp. 552–555 (2003)
Cham, T.-J., Rehg, J.M.: A multiple hypothesis approach to figure tracking. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR’99), Ft. Collins, CO, pp. 239–245 (1999)
Agarwal, A., Triggs, B.: Tracking articulated motion using a mixture of autoregressive models. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 54–65. Springer, Heidelberg (2004)
Roberts, T.J., McKenna, S.J., Ricketts, I.W.: Human pose estimation using learnt probabilistic region similarities and partial configurations. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 291–303. Springer, Heidelberg (2004)
Rehg, J.M., Morris, D.D.: Singularity analysis for articulated object tracking. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR’98), Santa Barbara, CA, pp. 289–297 (1998)
Kohler, M.: Using the kalman filter to track human interactive motion - modelling and initialization of the kalman filter for translational motion. Technical Report 629/1997, Fachbereich Informatik, Universität Dortmund, Dortmund, Germany (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Calvo, J., Patricio, M.A., Cuvillo, C., Usero, L. (2007). Context Information for Human Behavior Analysis and Prediction. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-73055-2_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73054-5
Online ISBN: 978-3-540-73055-2
eBook Packages: Computer ScienceComputer Science (R0)