By Topic

Learning and matching human activities using regular expressions

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Daldoss, M. ; Multimedia Signal Process. & Understanding Lab., Univ. of Trento, Trento, Italy ; Piotto, N. ; Conci, N. ; De Natale, F.G.B.

In this paper we propose a novel method to analyze trajectories in surveillance scenarios relying on automatically learned Context-Free Grammars. Given a training corpus of trajectories associated to a set of actions, an initial processing is carried out to extract the syntactical structure of the activities; then, the rules characterizing different behaviors are retrieved and coded as CFG models. The classification of the new trajectories vs the learned templates is performed through a parsing engine allowing the online recognition as well as the detection of nested activities. The proposed system has been validated in the framework of assisted living applications. The obtained results demonstrate the capability of the system in recognizing activity patterns in different configurations, also in presence of noise.

Published in:

Image Processing (ICIP), 2010 17th IEEE International Conference on

Date of Conference:

26-29 Sept. 2010