By Topic

Space-Time Behavior-Based Correlation-OR-How to Tell If Two Underlying Motion Fields Are Similar Without Computing Them?

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
$33 $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

2 Author(s)
Eli Shechtman ; Weizmann Inst. of Sci., Rehovot ; Michal Irani

We introduce a behavior-based similarity measure that tells us whether two different space-time intensity patterns of two different video segments could have resulted from a similar underlying motion field. This is done directly from the intensity information, without explicitly computing the underlying motions. Such a measure allows us to detect similarity between video segments of differently dressed people performing the same type of activity. It requires no foreground/background segmentation, no prior learning of activities, and no motion estimation or tracking. Using this behavior-based similarity measure, we extend the notion of two-dimensional image correlation into the three-dimensional space-time volume and thus allowing to correlate dynamic behaviors and actions. Small space-time video segments (small video clips) are "correlated" against the entire video sequences in all three dimensions (x, y, and t). Peak correlation values correspond to video locations with similar dynamic behaviors. Our approach can detect very complex behaviors in video sequences (for example, ballet movements, pool dives, and running water), even when multiple complex activities occur simultaneously within the field of view of the camera. We further show its robustness to small changes in scale and orientation of the correlated behavior.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:29 ,  Issue: 11 )