Nowadays, action recognition is widely applied in many fields. However, action is hard to
define by single modality information. The difference between image recognition and action …

In this paper, Spatio-Temporal Interest Points (STIPs) based technique is presented to
recognize human actions using Bag of Visual Words (BoVW) representation. First, we extract …

Video action recognition task involves modeling spatiotemporal information, and efficiency is
critical to capture spatiotemporal dependencies in the video. Most existing models rely on …

Recognizing human actions in videos is a challenging problem owning to complex motion
appearance, various backgrounds and semantic gap between low-level features and high …

Human action recognition is an important research topic in the field of computer vision due
to its application values. Recently, a variety of approaches based on deep learning features …

We propose a novel feature based on optical flow for action recognition. The feature is quite
simple and has much lower computational load than the existing features for action …

Recent video action recognition methods directly use RGB pixels in the compressed
domain. The cumbersome decoding process of traditional methods is avoided, enabling …

Recognizing human action is attractive research topic in computer vision since it plays an
important role on the applications such as human-computer interaction, intelligent …

In this paper, we handle the problem of human action recognition by combining covariance
matrices as local spatio-temporal (ST) descriptors and local ST features extracted densely …

We propose a general framework for fast and accurate recognition of actions in video using
empirical covariance matrices of features. A dense set of spatio-temporal feature vectors are …