Efficient Visual Event Detection using Volumetric Features

Yan Ke, Rahul Sukthankar, and Martial Hebert
International Conference on Computer Vision, October, 2005, pp. 166 - 173.


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Abstract
This paper studies the use of volumetric features as an alternative to popular local descriptor approaches for event detection in video sequences. Motivated by the recent success of similar ideas in object detection on static images, we generalize the notion of 2D box features to 3D spatio-temporal volumetric features. This general framework enables us to do real-time video analysis. We construct a real-time event detector for each action of interest by learning a cascade of filters based on volumetric features that efficiently scans video sequences in space and time. This event detector recognizes actions that are traditionally problematic for interest point methods -- such as smooth motions where insufficient space-time interest points are available. Our experiments demonstrate that the technique accurately detects actions on real-world sequences and is robust to changes in viewpoint, scale and action speed. We also adapt our technique to the related task of human action classification and confirm that it achieves performance comparable to a current interest point based human activity recognizer on a standard database of human activities.

Notes
Number of pages: 8

Text Reference
Yan Ke, Rahul Sukthankar, and Martial Hebert, "Efficient Visual Event Detection using Volumetric Features," International Conference on Computer Vision, October, 2005, pp. 166 - 173.

BibTeX Reference
@inproceedings{Sukthankar_2005_5165,
   author = "Yan Ke and Rahul Sukthankar and Martial Hebert",
   title = "Efficient Visual Event Detection using Volumetric Features",
   booktitle = "International Conference on Computer Vision",
   pages = "166 - 173",
   month = "October",
   year = "2005",
   volume = "1",
}