85 citations · 251 across the 11 of their papers we have counts for
11 papers
Video-based Human Action Recognition using Deep Learning: A Review
Hieu H. Pham, Louahdi Khoudour, Alain Crouzil +2
Human action recognition is an important application domain in computer vision. Its primary aim is to accurately describe human actions and their interactions from a previously uns…
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera
Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour +3
We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from RGB video sequences. Our approach proceeds along two stages. In…
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data
Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour +3
We present a new deep learning approach for real-time 3D human action recognition from skeletal data and apply it to develop a vision-based intelligent surveillance system. Given a…
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks
Huy-Hieu Pham, Louahdi Khoudour, Alain Crouzil +2
Recognizing human actions in untrimmed videos is an important challenging task. An effective 3D motion representation and a powerful learning model are two key factors influencing…
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks
Huy Hieu Pham, Louahdi Khoudour, Alain Crouzil +2
We propose a novel skeleton-based representation for 3D action recognition in videos using Deep Convolutional Neural Networks (D-CNNs). Two key issues have been addressed: First, h…
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks
Huy-Hieu Pham, Louahdi Khoudour, Alain Crouzil +2
Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc. Despite a…
Exploiting Spatio-Temporal Structure with Recurrent Winner-Take-All Networks
Eder Santana, Matthew Emigh, Pablo Zegers +1
We propose a convolutional recurrent neural network, with Winner-Take-All dropout for high dimensional unsupervised feature learning in multi-dimensional time series. We apply the…
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases
Pablo Huijse, Pablo A. Estevez, Pavlos Protopapas +2
Time-domain astronomy (TDA) is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new astronomical sky survey…
A Novel, Fully Automated Pipeline for Period Estimation in the EROS 2 Data Set
Pavlos Protopapas, Pablo Huijse, Pablo A. Estevez +2
We present a new method to discriminate periodic from non-periodic irregularly sampled lightcurves. We introduce a periodic kernel and maximize a similarity measure derived from in…
An Information Theoretic Algorithm for Finding Periodicities in Stellar Light Curves
Pablo Huijse, Pablo A. Estevez, Pavlos Protopapas +2
We propose a new information theoretic metric for finding periodicities in stellar light curves. Light curves are astronomical time series of brightness over time, and are characte…
Period Estimation in Astronomical Time Series Using Slotted Correntropy
Pablo Huijse, Pablo A. Estévez, Pablo Zegers +2
In this letter, we propose a method for period estimation in light curves from periodic variable stars using correntropy. Light curves are astronomical time series of stellar brigh…