Keep it accurate and diverse: Enhancing action recognition performance by ensemble learning

M.A. Bagheri, Q. Gao, S. Escalera, A. Clapes, K. Nasrollahi, M.B. Holte, T.B. Moeslund

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


The performance of different action recognition techniques has recently been studied by several computer vision researchers. However, the potential improvement in classification through classifier fusion by ensemble-based methods 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 perspective. 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 selection 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 performance, 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 classifiers are compared with those obtained from fusing the classifiers' output, showing enhanced performance of the proposed methodology.

Original languageEnglish
Title of host publicationIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Number of pages8
Publication date26. Oct 2015
ISBN (Print)21607516 21607508
Publication statusPublished - 26. Oct 2015


  • Accuracy
  • Computer vision
  • Feature extraction
  • Histograms
  • Trajectory
  • Videos
  • Visualization


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