  This paper propose a novel dictionary learning approach to detect event
action using skeletal information extracted from RGBD video. The event action
is represented as several latent atoms and composed of latent spatial and
temporal attributes. We perform the method at the example of fall event
detection. The skeleton frames are clustered by an initial K-means method. Each
skeleton frame is assigned with a varying weight parameter and fed into our
Gradual Online Dictionary Learning (GODL) algorithm. During the training
process, outlier frames will be gradually filtered by reducing the weight that
is inversely proportional to a cost. In order to strictly distinguish the event
action from similar actions and robustly acquire its action unit, we build a
latent unit temporal structure for each sub-action. We evaluate the proposed
method on parts of the NTURGB+D dataset, which includes 209 fall videos, 405
ground-lift videos, 420 sit-down videos, and 280 videos of 46 otheractions. We
present the experimental validation of the achieved accuracy, recall and
precision. Our approach achieves the bestperformance on precision and accuracy
of human fall event detection, compared with other existing dictionary learning
methods. With increasing noise ratio, our method remains the highest accuracy
and the lowest variance.
