Type

Conference Proceedings

Authors

Noel E. O'Connor
Cem Direkoglu
Suzanne Little
Iveel Jargalsaikhan

Subjects

Computer Science

Topics
image processing support vector machine motion human action recognition interest digital video human activities state of the art

Action recognition based on sparse motion trajectories (2013)

Abstract We present a method that extracts effective features in videos for human action recognition. The proposed method analyses the 3D volumes along the sparse motion trajectories of a set of interest points from the video scene. To represent human actions, we generate a Bag-of-Features (BoF) model based on extracted features, and finally a support vector machine is used to classify human activities. Evaluation shows that the proposed features are discriminative and computationally efficient. Our method achieves state-of-the-art performance with the standard human action recognition benchmarks, namely KTH and Weizmann datasets.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> Subject = Computer Science: Image processing
Ireland -> Dublin City University -> Subject = Computer Science
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: CLARITY: The Centre for Sensor Web Technologies
Ireland -> Dublin City University -> Subject = Computer Science: Digital video
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres

Full list of authors on original publication

Noel E. O'Connor, Cem Direkoglu, Suzanne Little, Iveel Jargalsaikhan

Experts in our system

1
Noel E. O'Connor
Dublin City University
Total Publications: 474
 
2
Cem Direkoglu
Dublin City University
Total Publications: 9
 
3
Suzanne Little
Dublin City University
Total Publications: 36
 
4
Iveel Jargalsaikhan
Dublin City University
Total Publications: 9