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2013 | OriginalPaper | Buchkapitel

6. Activity Detection Using Regular Expressions

verfasst von : Mattia Daldoss, Nicola Piotto, Nicola Conci, Francesco G. B. De Natale

Erschienen in: Analysis, Retrieval and Delivery of Multimedia Content

Verlag: Springer New York

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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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Metadaten
Titel
Activity Detection Using Regular Expressions
verfasst von
Mattia Daldoss
Nicola Piotto
Nicola Conci
Francesco G. B. De Natale
Copyright-Jahr
2013
Verlag
Springer New York
DOI
https://doi.org/10.1007/978-1-4614-3831-1_6

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