Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos
Author
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-29
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
Both static features and motion features have shown promising performance in human activities recognition task.
However, the information included in these features is insufficient for complex human activities.
In this paper, we propose extracting relational information of static features and motion features for human activities recognition.
The videos are represented by a classical Bag-of-Word (BoW) model which is useful in many works.
To get a compact and discriminative codebook with small dimension, we employ the divisive algorithm based on KL-divergence to reconstruct the codebook.
After that, to further capture strong relational information, we construct a bipartite graph to model the relationship between words of different feature set.
Then we use a k -way partition to create a new codebook in which similar words are getting together.
With this new codebook, videos can be represented by a new BoW vector with strong relational information.
Moreover, we propose a method to compute new clusters from the divisive algorithm’s projective function.
We test our work on the several datasets and obtain very promising results.
American Psychological Association (APA)
Yao, Li. 2016. Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099589
Modern Language Association (MLA)
Yao, Li. Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-7.
https://search.emarefa.net/detail/BIM-1099589
American Medical Association (AMA)
Yao, Li. Extract the Relational Information of Static Features and Motion Features for Human Activities Recognition in Videos. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-7.
https://search.emarefa.net/detail/BIM-1099589
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099589