Human behavior recognition using a context-free grammar

Andrea Rosani; Nicola Conci; Francesco G. De Natale

doi:10.1117/1.JEI.23.3.033016

20 June 2014 Human behavior recognition using a context-free grammar

Andrea Rosani, Nicola Conci, Francesco G. De Natale

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 3, 033016 (June 2014). https://doi.org/10.1117/1.JEI.23.3.033016

Abstract

Automatic recognition of human activities and behaviors is still a challenging problem for many reasons, including limited accuracy of the data acquired by sensing devices, high variability of human behaviors, and gap between visual appearance and scene semantics. Symbolic approaches can significantly simplify the analysis and turn raw data into chains of meaningful patterns. This allows getting rid of most of the clutter produced by low-level processing operations, embedding significant contextual information into the data, as well as using simple syntactic approaches to perform the matching between incoming sequences and models. We propose a symbolic approach to learn and detect complex activities through the sequences of atomic actions. Compared to previous methods based on context-free grammars, we introduce several important novelties, such as the capability to learn actions based on both positive and negative samples, the possibility of efficiently retraining the system in the presence of misclassified or unrecognized events, and the use of a parsing procedure that allows correct detection of the activities also when they are concatenated and/or nested one with each other. An experimental validation on three datasets with different characteristics demonstrates the robustness of the approach in classifying complex human behaviors.

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Andrea Rosani, Nicola Conci, and Francesco G. De Natale "Human behavior recognition using a context-free grammar," Journal of Electronic Imaging 23(3), 033016 (20 June 2014). https://doi.org/10.1117/1.JEI.23.3.033016

Published: 20 June 2014

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