Spatiotemporal human representation based on 3D visual perception data is a rapidly
growing research area. Representations can be broadly categorized into two groups …

Human action recognition is an important yet challenging task. The recently developed
commodity depth sensors open up new possibilities of dealing with this problem but also …

Human activity recognition (HAR) is a complex and multifaceted problem. The research
community has reported numerous approaches to perform HAR. Along with HAR …

We present a new descriptor for activity recognition from videos acquired by a depth sensor.
Previous descriptors mostly compute shape and motion features independently; thus, they …

Human action recognition from videos is a challenging machine vision task with multiple
important application domains, such as humanrobot/machine interaction, interactive …

Human action recognition is an important yet challenging task. Human actions usually
involve human-object interactions, highly articulated motions, high intra-class variations, and …

In this paper, we propose an effective method to recognize human actions using 3D skeleton
joints recovered from 3D depth data of RGBD cameras. We design a new action feature …

We propose a set of features derived from skeleton tracking of the human body and depth
maps for the purpose of action recognition. The descriptors proposed are easy to implement …

Human pose estimation has been actively studied for decades. While traditional approaches
rely on 2d data like images or videos, the development of Time-of-Flight cameras and other …

Abstract This paper presents Space-Time Occupancy Patterns (STOP), a new visual
representation for 3D action recognition from sequences of depth maps. In this new …