Abstract
The performance of different action recognition tech- niques has recently been studied by several computer vision researchers. However, the potential improvement in classi- fication through classifier fusion by ensemble-based meth- ods has remained unattended. In this work, we evaluate the performance of an ensemble of action learning techniques, each performing the recognition task from a different per- spective. The underlying idea is that instead of aiming a very sophisticated and powerful representation/learning technique, we can learn action categories using a set of relatively simple and diverse classifiers, each trained with different feature set. In addition, combining the outputs of several learners can reduce the risk of an unfortunate selec- tion of a learner on an unseen action recognition scenario. This leads to having a more robust and general-applicable framework. In order to improve the recognition perfor- mance, a powerful combination strategy is utilized based on the Dempster-Shafer theory, which can effectively make use of diversity of base learners trained on different sources of information. The recognition results of the individual clas- sifiers are compared with those obtained from fusing the classifiers’ output, showing enhanced performance of the proposed methodology.
Original language | English |
---|---|
Title of host publication | IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), 2015 |
Publisher | IEEE Computer Society Press |
Publication date | 7 Jun 2015 |
Pages | 22-29 |
ISBN (Print) | 978-1-4673-6759-2 |
DOIs | |
Publication status | Published - 7 Jun 2015 |
Event | 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshop: Looking at People - Boston, United States Duration: 6 Jun 2015 → 12 Jun 2015 |
Conference
Conference | 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshop |
---|---|
Country/Territory | United States |
City | Boston |
Period | 06/06/2015 → 12/06/2015 |