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Proceedings Paper

A new point process model for trajectory-based events annotation
Author(s): Nicolas Ballas; Bertrand Delezoide; Françoise Prêteux
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Paper Abstract

Human actions annotation in videos has received an increase attention from the scientific community these last years mainly due to its large implication in many computer vision applications. The current leading paradigm to perform human actions annotation is based on local features. Local features robust to geometric transformations and occlusion are extracted from a video and aggregated to obtain a global video signature. However, current aggregation schemes such as Bag-of-Words or spatio-temporal grids have no or limited information about the local features spatio-temporal localization in videos. It has been shown that local features localization can be hepful for detecting a concept or an action. In this work we improve on the aggregation step by embedding local features spatio-temporal information in the final video representation by introducing a point process model. We propose an event recognition system involving two main steps: (1) local features extraction based on robust point trajectories, and (2) a global action representation capturing the spatio-temporal context information through an innovative point process clustering. A point process provides indeed a well-defined formalism to characterize local features localization along with their interactions information. Results are evaluated on the HOllywood in Human Action (HOHA) dataset showing an improvement over the state-of-art.

Paper Details

Date Published: 2 February 2012
PDF: 12 pages
Proc. SPIE 8300, Image Processing: Machine Vision Applications V, 83000B (2 February 2012); doi: 10.1117/12.912088
Show Author Affiliations
Nicolas Ballas, CEA LIST (France)
Mines ParisTech (France)
Bertrand Delezoide, CEA LIST (France)
Françoise Prêteux, Mines ParisTech (France)

Published in SPIE Proceedings Vol. 8300:
Image Processing: Machine Vision Applications V
Philip R. Bingham; Edmund Y. Lam, Editor(s)

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