Dynamic time warping-based features with class-specific joint importance maps for action recognition using Kinect depth sensor
H Mohammadzade, S Hosseini… - IEEE Sensors …, 2021 - ieeexplore.ieee.org
This paper proposes a novel 3D action recognition technique that uses time-series
information extracted from depth image sequences for use in systems of human daily activity …
information extracted from depth image sequences for use in systems of human daily activity …
A convolutional autoencoder model with weighted multi-scale attention modules for 3D skeleton-based action recognition
The 3D skeleton sequences of action can be recognized based on series of meaningful
movements including changes in the direction and geometry features of the body pose. In …
movements including changes in the direction and geometry features of the body pose. In …
Human activity discovery with automatic multi-objective particle swarm optimization clustering with gaussian mutation and game theory
Despite many advances in Human Activity Recognition (HAR), most existing works are
conducted with supervision. Supervised methods rely on labeled training data. However …
conducted with supervision. Supervised methods rely on labeled training data. However …
Multi-stream CNNs with orientation-magnitude response maps and weighted inception module for human action recognition
In this paper, a multi-stream convolutional neural network (CNN) is proposed, which is
integrated with multi-modal data acquired from a video camera, Kinect and wearable inertial …
integrated with multi-modal data acquired from a video camera, Kinect and wearable inertial …
Spatio‐temporal attention modules in orientation‐magnitude‐response guided multi‐stream CNNs for human action recognition
This paper introduces a new descriptor called orientation‐magnitude response maps as a
single 2D image to effectively explore motion patterns. Moreover, boosted multi‐stream CNN …
single 2D image to effectively explore motion patterns. Moreover, boosted multi‐stream CNN …
[PDF][PDF] A comparative study of supervised and unsupervised approaches in human activity analysis based on skeleton data
One of the important areas of machine intelligence research today is human activity
recognition (HAR), with the goal of automatically identifying human activities from various …
recognition (HAR), with the goal of automatically identifying human activities from various …
[HTML][HTML] Flexible multi-objective particle swarm optimization clustering with game theory to address human activity discovery fully unsupervised
Human activity recognition is a crucial field of study, but current approaches often require
ground truth labels, which are not always available. We propose a new method called the …
ground truth labels, which are not always available. We propose a new method called the …
A dynamic time warping based kernel for 3D action recognition using Kinect depth sensor
M Akyash, H Mohammadzade… - 2020 28th Iranian …, 2020 - ieeexplore.ieee.org
Since the advent of 3D sensors and the introduction of robust algorithms for extracting
position of skeletal joints out of RGB images, 3D action recognition has attracted a lot of …
position of skeletal joints out of RGB images, 3D action recognition has attracted a lot of …
Investigation of the unsupervised machine learning techniques for human activity discovery
Human activity recognition has been considered as the main capability of an intelligent
system in understanding of human activities. Human activity recognition focuses on …
system in understanding of human activities. Human activity recognition focuses on …
Flexible Multi-Objective Particle Swarm Optimization Clustering with Game Theory to Address Human Activity Recognition Fully Unsupervised
Most research in human activity recognition is supervised, while non-supervised
approaches are not completely unsupervised. Moreover, These methods cannot be used in …
approaches are not completely unsupervised. Moreover, These methods cannot be used in …