Video classification is computationally expensive. In this paper, we address theproblem of frame selection to reduce the computational cost video classification.Recent work has successfully leveraged for long, untrimmed videos,where much content not relevant, and easy discard. work, however,we focus on more standard short, trimmed problem. Weargue that good can only videoclassification but also...

We present a robust skeleton-based action recognition method with graph convolutional network (GCN) that uses the new adjacency matrix, called Rank-GCN. In Rank-GCN, biggest change from previous approaches is how matrix generated to accumulate features neighboring nodes by re-defining “adjacency.” The which we call rank ranking all according metrics including Euclidean distance of...

Recognizing human actions is an important and challenging topic in computer vision, with many important applications including video surveillance, video indexing and understanding of social interaction. From a computational perspective, actions can be defined as four-dimensional patterns, in space and in time. Such patterns can be modeled using several representations which differ from each oth...

uman action recognition has evoked considerable interest in the various research areas and applications due to its potential use in proactive computing. The objective of this work is to recognize various human actions like run, jump, walk etc. Moving Object detection and tracking is the first step for action recognition. The algorithm first makes use of the statistical background model and back...

Human Action Recognition Human action recognition is an important topic of computer vision research and applications. The goal of the action recognition is an automated analysis of ongoing events from video data. A reliable system capable of recognizing various human actions has many important applications. The applications include surveillance systems, health-care systems, and a variety of sys...

In this paper, we present an efficient system for action recognition from very short sequences. For action recognition typically appearance and/or motion information of an action is analyzed using a large number of frames. This is a limitation if very fast actions (e.g., in sport analysis) have to be analyzed. To overcome this limitation, we propose a method that uses a single-frame representat...