Regular Articles

Human behavior recognition using a context-free grammar

[+] Author Affiliations
Andrea Rosani

University of Trento, Department of Information Engineering and Computer Science, DISI, I-38121 Trento, Italy

Nicola Conci

University of Trento, Department of Information Engineering and Computer Science, DISI, I-38121 Trento, Italy

Francesco G. B. De Natale

University of Trento, Department of Information Engineering and Computer Science, DISI, I-38121 Trento, Italy

J. Electron. Imaging. 23(3), 033016 (Jun 20, 2014). doi:10.1117/1.JEI.23.3.033016
History: Received March 3, 2014; Revised May 9, 2014; Accepted May 22, 2014
Text Size: A A A

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.

Figures in this Article
© 2014 SPIE and IS&T

Citation

Andrea Rosani ; Nicola Conci and Francesco G. B. De Natale
"Human behavior recognition using a context-free grammar", J. Electron. Imaging. 23(3), 033016 (Jun 20, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.3.033016


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.