The low-cost depth cameras have facilitated the research of human action recognition in the
last decades. Despite various approaches have been presented to improve the recognition …

Most deep learning-based action recognition models focus only on short-term motions, so
the model often causes misjudgments of actions that are combined by multiple processes …

Human activity analysis in videos has increasingly attracted attention in computer vision
research with the massive number of videos now accessible online. Although many …

In this paper, a novel method based on the two-stream dictionary learning architecture for
human action recognition is proposed. The architecture consists of interest patch (IP) …

Stacked autoencoder (SAE) provides excellent performance for image processing under
sufficient training samples. However, the collection of training samples is difficult in …

Effective spatial-temporal representation of motion information is crucial to human action
classification. In spite of the attempt of most existing methods capturing spatial-temporal …

Max-min distance analysis (MMDA) performs dimensionality reduction by maximizing the
minimum pairwise distance between classes in the latent subspace under the …

In this paper, we address the problem of action recognition from still images and videos.
Traditional local features such as SIFT and STIP invariably pose two potential problems: 1) …

A human action recognition method is reported in which pose representation is based on the
contour points of the human silhouette and actions are learned by a strict 3d pyramidal …

As a natural extension of Compressive Sensing and the requirement of some practical
problems, Phaseless Compressive Sensing (PCS) has been introduced and studied …