Human activities recognition using depth images

59 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks (2014, background): “A batch of works on human action/activity understanding mainly focused on developing robust and descriptive features [35, 6, 20, 19, 40, 37, 24].”
Skeleton-based Human Activity Recognition for Video Surveillance (2015, methodology): “Gupta et al. [18] prsent a method to classify human activities by leveraging on the cues available from depth images alone.”
Activity Recognition for Natural Human Robot Interaction (2014, unknown): “al[6] 78.”
Explore Efficient Local Features from RGB-D Data for One-Shot Learning Gesture Recognition (2016, unknown): “2013 [65] 78.”
3D human action segmentation and recognition using pose kinetic energy (2014, unknown): “5 NA Depth Images Segment [12] 78.”
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps With Accurate Object Boundaries (2018, background): “The problem of estimating the depth map of a scene from its single image has attracted a lot of attention in the field of computer vision, since depth maps have a lot of applications, such as augmented reality [22], human computer interaction [33], human activity recognition [12], scene recognition ”