Human action recognition based on semi-supervised discriminant analysis with global constraint
Human action recognition is an important area in computer vision and pattern recognition. Human joint position data are regarded as the most effective feature for this task. Depth camera using fringe projection techniques and related software provides us the capability to generate a large amount of human joint position data. However, these data cannot be used as the training data for supervised learning before the action labels are given, and manually labeling all the data is quite time-consuming. In this paper, we propose a novel algorithm named semi-supervised discriminant analysis with global constraint (SDG) which can better estimate the data distribution with both insufficient labeled data and sufficient unlabeled data. We use public mocap dataset HumanEva which is obtained by marker-based motion capture system, and our proposed skeleton dataset captured by depth camera for the evaluation. Experimental results demonstrate the effectiveness of our algorithm.
action recognition semi-supervised learning dimensionality reduction mocap data skeleton data