Abstract
In this chapter we propose a novel method to analyze trajectories in surveillance scenarios by means of Context-Free Grammars (CFGs). Given a training corpus of trajectories associated to a set of actions, a preliminary processing phase is carried out to characterize the paths as sequences of symbols. This representation turns the numerical representation of the coordinates into a syntactical description of the activity structure, which is successively adopted to identify different behaviors through the CFG models. Such a modeling is the basis for the classification and matching of new trajectories versus the learned templates and it is carried out through a parsing engine that enables the online recognition of human activities. An additional module is provided to recover parsing errors (i.e., insertion, deletion, or substitution of symbols) and update the activity models previously learned. The proposed system has been validated in indoor, in an assisted living context, demonstrating good capabilities in recognizing activity patterns in different configurations, and in particular in presence of noise in the acquired trajectories, or in case of concatenated and nested actions.
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Acknowledgments
The research has been developed under the project ACube funded by the Provincia Autonoma di Trento (Italy).
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Daldoss, M., Piotto, N., Conci, N., De Natale, F.G.B. (2013). Activity Detection Using Regular Expressions. In: Adami, N., Cavallaro, A., Leonardi, R., Migliorati, P. (eds) Analysis, Retrieval and Delivery of Multimedia Content. Lecture Notes in Electrical Engineering, vol 158. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3831-1_6
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DOI: https://doi.org/10.1007/978-1-4614-3831-1_6
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