Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
10-2014
Abstract
Human action recognition is challenging mainly due to intro-variety, inter-ambiguity and clutter backgrounds in real videos. Bag-of-visual words model utilizes spatio-temporal interest points(STIPs), and represents action by the distribution of points which ignores visual context among points. To add more contextual information, we propose a method by encoding spatio-temporal distribution of weighted pairwise points. First, STIPs are extracted from an action sequence and clustered into visual words. Then, each word is weighted in both temporal and spatial domains to capture the relationships with other words. Finally, the directional relationships between co-occurrence pairwise words are used to encode visual contexts. We report state-of-the-art results on Rochester and UT-Interaction datasets to validate that our method can classify human actions with high accuracies.
Keywords
Human action recognition, spatio-temporal interest points, bag-of-words
Discipline
Computer Engineering | Software Engineering
Research Areas
Data Science and Engineering
Publication
2014 IEEE International Conference on Image Processing (ICIP 2014), Paris, October 27-30
First Page
1460
Last Page
1464
Identifier
10.1109/ICIP.2014.7025292
Publisher
IEEE
City or Country
Paris
Citation
LIU, Mengyuan; LIU, Hong; and SUN, Qianru.
Action classification by exploring directional co-occurrence of weighted STIPs. (2014). 2014 IEEE International Conference on Image Processing (ICIP 2014), Paris, October 27-30. 1460-1464.
Available at: https://ink.library.smu.edu.sg/sis_research/4463
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICIP.2014.7025292