Human activity recognition using multi-features and multiple kernel learning
- 1 May 2014
- journal article
- Published by Elsevier BV in Pattern Recognition
- Vol. 47 (5), 1800-1812
- https://doi.org/10.1016/j.patcog.2013.11.032
Abstract
No abstract availableThis publication has 28 references indexed in Scilit:
- Recognizing actions using depth motion maps-based histograms of oriented gradientsPublished by Association for Computing Machinery (ACM) ,2012
- View invariant human action recognition using histograms of 3D jointsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- EigenJoints-based action recognition using Naïve-Bayes-Nearest-NeighborPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Fusing appearance and distribution information of interest points for action recognitionPattern Recognition, 2012
- Action Recognition by Multiple Features and Hyper-Sphere Multi-class SVMPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Action recognition based on a bag of 3D pointsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Object, Scene and Actions: Combining Multiple Features for Human Action RecognitionLecture Notes in Computer Science, 2010
- Action recognition via multi-feature fusion and Gaussian process classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- A differential geometric approach to representing the human actionsComputer Vision and Image Understanding, 2008
- Actions as space-time shapesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005