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Action recognition in video using a spatial-temporal graph-based feature representation

Jargalsaikhan, Iveel, Little, Suzanne orcid logoORCID: 0000-0003-3281-3471, Trichet, Remi and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2015) Action recognition in video using a spatial-temporal graph-based feature representation. In: 12th IEEE International Conference on Advanced Video and Signal based Surveillance (AVSS2015), 25-26 Aug 2015, Karlsruhe, Germany.

Abstract
We propose a video graph based human action recognition framework. Given an input video sequence, we extract spatio-temporal local features and construct a video graph to incorporate appearance and motion constraints to reflect the spatio-temporal dependencies among features. them. In particular, we extend a popular dbscan density-based clustering algorithm to form an intuitive video graph. During training, we estimate a linear SVM classifier using the standard Bag-of-words method. During classification, we apply Graph-Cut optimization to find the most frequent action label in the constructed graph and assign this label to the test video sequence. The proposed approach achieves stateof-the-art performance with standard human action recognition benchmarks, namely KTH and UCF-sports datasets and competitive results for the Hollywood (HOHA) dataset.
Metadata
Item Type:Conference or Workshop Item (Speech)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Video surveillance; Action recognition
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:20721
Deposited On:01 Sep 2015 09:57 by Remi Trichet . Last Modified 19 Oct 2018 09:51
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