Publications de Michael Villamizar

Pose Transformers (POTR): Human Motion Prediction with Non-Autoregressive Transformers

We propose to leverage Transformer architectures for non-autoregressive human motion prediction. Our approach decodes elements in parallel from a query sequence, instead of conditioning on previous predictions such as in state-of-the-art RNN-based approach ...

IEEE COMPUTER SOC2021

Multi-scale sequential network for semantic text segmentation and localization

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We present a novel method for semantic text document analysis which in addition to localizing text it labels the text in user-defined semantic categories. More precisely, it consists of a fully-convolutional and sequential network that we apply to the part ...

2020

WatchNet plus plus : efficient and accurate depth-based network for detecting people attacks and intrusion

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not ...

2020

Residual Pose: A Decoupled Approach for Depth-based 3D Human Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

IEEE2020

Efficient Convolutional Neural Networks for Depth-Based Multi-Person Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

Achieving robust multi-person 2D body landmark localization and pose estimation is essential for human behavior and interaction understanding as encountered for instance in HRI settings. Accurate methods have been proposed recently, but they usually rely o ...

2020

Investigating Depth Domain Adaptation for Efficient Human Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

Convolutional Neural Networks (CNN) are the leading models for human body landmark detection from RGB vision data. However, as such models require high computational load, an alternative is to rely on depth images which, due to their more simple nature, ca ...

SPRINGER INTERNATIONAL PUBLISHING AG2018

Real-time Convolutional Networks for Depth-based Human Pose Estimation

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We propose to combine recent Convolutional Neural Networks (CNN) models with depth imaging to obtain a reliable and fast multi-person pose estimation algorithm applicable to Human Robot Interaction (HRI) scenarios. Our hypothesis is that depth images conta ...

IEEE2018

WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems

Jean-Marc Odobez, Olivier Canévet, Michael Villamizar

We propose a deep-learning approach for people detection on depth imagery. The approach is designed to be deployed as an autonomous appliance for identifying people attacks and intrusion in video surveillance scenarios. To this end, we propose a fully-conv ...

IEEE2018