Abstract
The Ambient Intelligence (AmI) paradigm requires a robust interpretation of people actions and behaviour and a way for automatically generating persistent spatial-temporal models of recurring events. This paper describes a relatively inexpensive technique that does not require the use of conventional trackers to identify the main paths of highly cluttered scenes, approximating them with spline curves. An AmI system could easily make use of the generated model to identify people who do not follow prefixed paths and warn them. Security, safety, rehabilitation are potential application areas. The model is evaluated against new data of the same scene.
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Zhan, B., Remagnino, P., Velastin, S.A. (2005). Mining Paths of Complex Crowd Scenes. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds) Advances in Visual Computing. ISVC 2005. Lecture Notes in Computer Science, vol 3804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595755_16
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DOI: https://doi.org/10.1007/11595755_16
Publisher Name: Springer, Berlin, Heidelberg
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