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Action recognition based on sparse motion trajectories

Jargalsaikhan, Iveel, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471, Direkoglu, Cem and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2013) Action recognition based on sparse motion trajectories. In: IEEE International Conference on Image Processing, 15-18 Sept 2013, Melbourne, Australia.

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.
Metadata
Item Type:Conference or Workshop Item (Poster)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:human action recognition
Subjects:Computer Science > Image processing
Computer Science > Digital video
DCU Faculties and Centres:Research Initiatives and Centres > CLARITY: The Centre for Sensor Web Technologies
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Framework Programme 7 (285621)
ID Code:19258
Deposited On:17 Sep 2013 14:53 by Suzanne Little . Last Modified 19 Oct 2018 14:57
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