Abstract
In this paper, we are interested in detecting action attributes from sports videos for event understanding and video analysis. Action attribute is a middle layer between low level motion features and high level action classes, which includes various motion patterns of human limbs and bodies and the interaction between human and objects. Successfully detecting action attributes provides a richer video description that facilitates many other important tasks, such action classification, video understanding, automatic video transcript, etc. A naive approach to deal with this challenging problem is to train a classifier for each attribute and then use them to detect attributes in novel videos independently. However, this independence assumption is often too strong, and as we show in our experiments, produces a large number of false positives in practice. We propose a novel approach that incorporates the contextual constraints for activity attribute detection. The temporal contexts within an attribute and the co-occurrence contexts between different attributes are modelled by a factorial conditional random field, which encourages agreement between different time points and attributes. The effectiveness of our methods are clearly illustrated by the experimental evaluations.
Original language | English (US) |
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Title of host publication | BMVC 2013 - Electronic Proceedings of the British Machine Vision Conference 2013 |
Publisher | British Machine Vision Association, BMVA |
DOIs | |
State | Published - 2013 |
Externally published | Yes |
Event | 2013 24th British Machine Vision Conference, BMVC 2013 - Bristol, United Kingdom Duration: Sep 9 2013 → Sep 13 2013 |
Other
Other | 2013 24th British Machine Vision Conference, BMVC 2013 |
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Country/Territory | United Kingdom |
City | Bristol |
Period | 9/9/13 → 9/13/13 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition