Human Activity Recognition as Time-Series Analysis
Open Access
- 11 October 2015
- journal article
- research article
- Published by Hindawi Limited in Mathematical Problems in Engineering
- Vol. 2015, 1-9
- https://doi.org/10.1155/2015/676090
Abstract
We propose a system that can recognize daily human activities with a Kinect-style depth camera. Our system utilizes a set of view-invariant features and the hidden state conditional random field (HCRF) model to recognize human activities from the 3D body pose stream provided by MS Kinect API or OpenNI. Many high-level daily activities can be regarded as having a hierarchical structure where multiple subactivities are performed sequentially or iteratively. In order to model effectively these high-level daily activities, we utilized a multiclass HCRF model, which is a kind of probabilistic graphical models. In addition, in order to get view-invariant, but more informative features, we extract joint angles from the subject’s skeleton model and then perform the feature transformation to obtain three different types of features regarding motion, structure, and hand positions. Through various experiments using two different datasets, KAD-30 and CAD-60, the high performance of our system is verified.Keywords
This publication has 8 references indexed in Scilit:
- Probabilistic Models for Local Patterns AnalysisJournal of Information Processing Systems, 2014
- Flexible context aware interface for ambient assisted livingHuman-centric Computing and Information Sciences, 2014
- An Intelligent Automatic Early Detection System of Forest Fire Smoke Signatures using Gaussian Mixture ModelJournal of Information Processing Systems, 2013
- Small Object Segmentation Based on Visual Saliency in Natural ImagesJournal of Information Processing Systems, 2013
- Collective intelligence within web videoHuman-centric Computing and Information Sciences, 2013
- Intention awareness: improving upon situation awareness in human-centric environmentsHuman-centric Computing and Information Sciences, 2013
- Depth video-based human activity recognition system using translation and scaling invariant features for life logging at smart homeIEEE Transactions on Consumer Electronics, 2012
- Human Activity Recognition Using Body Joint-Angle Features and Hidden Markov ModelETRI Journal, 2011