A new point process model for trajectory-based events annotation

Nicolas Ballas; Bertrand Delezoide; Françoise Prêteux

doi:10.1117/12.912088

2 February 2012 A new point process model for trajectory-based events annotation

Nicolas Ballas, Bertrand Delezoide, Françoise Prêteux

Proceedings Volume 8300, Image Processing: Machine Vision Applications V; 83000B (2012) https://doi.org/10.1117/12.912088
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

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.

Citation Download Citation

Nicolas Ballas, Bertrand Delezoide, and Françoise Prêteux "A new point process model for trajectory-based events annotation", Proc. SPIE 8300, Image Processing: Machine Vision Applications V, 83000B (2 February 2012); https://doi.org/10.1117/12.912088

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available