Human Activity Recognition in Real-Times Environments using Skeleton Joints

Human Activity Recognition in Real-Times Environments using Skeleton Joints

Informative joints based human action recognition using skeleton contexts

The launching of Microsoft Kinect with skeleton tracking technique opens up new potentials for skeleton based human action recognition. However, the 3D human skeletons, generated via skeleton tracking from the depth map sequences, are generally very noisy and unreliable. In this paper, we introduce a robust informative joints based human action recognition method. Inspired by the instinct of th...

Human Activity Recognition in AAL Environments Using Random Projections

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Livi...

Human Action Recognition with an Incomplete Real - Time Pose Skeleton

Currently, most human action recognition systems are trained with feature sets that have no missing data. Unfortunately, the use of human pose estimation models to provide more descriptive features also entails an increased sensitivity to occlusions, meaning that incomplete feature information will be unavoidable for realistic scenarios. To address the problem of occlusions, this paper proposes...

Skeleton-based Human Activity Recognition for Video Surveillance

Recognizing human activity is one of the important areas of computer vision research today. It plays a vital role in constructing intelligent surveillance systems. Despite the efforts in the past decades, recognizing human activities from videos is still a challenging task. Human activity may have different forms ranging from simple actions to complex activities. Recently released depth cameras...