Action Attribute Detection from Sports Videos with Contextual Constraints

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.

Olympic sports attribute data set

16 sports activities, several videos for each activity.

ZIP file, video frames: UMD Olympic Sports Attribute Data Set, IMAGE

ZIP file, annotation in XML format: UMD Olympic Sports Attribute Data Set, Annotation

Readme file: To Be Added

Relevant publication:

Xiaodong Yu, Ching Lik Teo, Yezhou Yang, Cornelia Fermüller, Yiannis Aloimonos. Action Attribute Detection from Sports Videos with Contextual Constraints.British Machine Vision Conference (BMVC)

  • @inproceedings{yu2013action,
  • title={Action Attribute Detection from Sports Videos with Contextual Constraints},
  • author={Yu, Xiaodong and Teo, Ching Lik and Yang, Yezhou and Ferm{\"u}ller, Cornelia and Aloimonos, Yiannis}
  • booktitle={British Machine Vision Conference},
  • year={2013}
  • }

    Questions? Please contact yzyang "at" cs dot umd dot edu