Abstract
This work describes an approach for the interpretation and explanation of human behavior in image sequences, within the context of a Cognitive Vision System. The information source is the geometrical data obtained by applying tracking algorithms to an image sequence, which is used to generate conceptual data. The spatial characteristics of the scene are automatically extracted from the resuling tracking trajectories obtained during a training period. Interpretation is achieved by means of a rule-based inference engine called Fuzzy Metric Temporal Horn Logic and a behavior modeling tool called Situation Graph Tree. These tools are used to generate conceptual descriptions which semantically describe observed behaviors.
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References
Buxton, H.: Learning and understanding dynamic scene activity: A review. Image and Vision Computing 21(1), 125–136 (2002)
Fernyhough, J., Cohn, A., Hogg, D.: Constructing qualitative event models automatically from video input. Image and Vision Computing 18, 81–103 (2000)
Galata, A., Johnson, N., Hogg, D.: Learning variable-length markov models of behavior. Computer Vision and Image Understanding 81(3), 398–413 (2001)
Haag, M., Nagel, H.-H.: Incremental recognition of traffic situations from video image sequences. Image and Vision Computing 18(2), 137–153 (2000)
Horprasert, T., Harwood, D., Davis, L.: A Robust Background Subtraction and Shadow Detection. In: 4th ACCV, Taipei, Taiwan, vol. 1, pp. 983–988 (2000)
Intille, S.S., Bobick, A.F.: Recognized planned, multiperson action. International Journal of Computer Vision 81(3), 414–445 (2001)
Kojima, A., Tamura, T., Fukunaga, K.: Natural language description of human activities from video images based on concept hierarchy of actions. International Journal of Computer Vision 50(2), 171–184 (2002)
Morris, R.J., Hogg, D.C.: Statistical models of object interaction. International Journal of Computer Vision 37(2), 209–215 (2000)
Nagel, H.-H.: From image sequences towards conceptual descriptions. Image and Vision Computing 6(2), 59–74 (1988)
Remagnino, P., Tan, T., Baker, K.: Agent oriented annotation in model based visual surveillance. In: Proceedings of International Conference on Computer Vision (ICCV’98), Mumbai, India, pp. 857–862 (1998)
Rowe, D., Rius, I., González, J., Villanueva, J.J.: Improving tracking by handling occlusions. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 384–393. Springer, Heidelberg (2005)
Schäfer, K.: Fuzzy spatio-temporal logic programming. In: Brzoska, C. (ed.) Proceedings of 7th Workshop in Temporal and Non-Classical Logics – IJCAI’97, Nagoya, Japan, pp. 23–28 (1997)
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Baiget, P., Fernández, C., Roca, X., Gonzàlez, J. (2007). Automatic Learning of Conceptual Knowledge in Image Sequences for Human Behavior Interpretation. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_65
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DOI: https://doi.org/10.1007/978-3-540-72847-4_65
Publisher Name: Springer, Berlin, Heidelberg
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