22 citations · 50 across the 4 of their papers we have counts for
5 papers · 1 filter
Towards Dense People Detection with Deep Learning and Depth images
David Fuentes-Jimenez, Cristina Losada-Gutierrez, David Casillas-Perez +4
This paper proposes a DNN-based system that detects multiple people from a single depth image. Our neural network processes a depth image and outputs a likelihood map in image coor…
Exploiting the ConvLSTM: Human Action Recognition using Raw Depth Video-Based Recurrent Neural Networks
Adrian Sanchez-Caballero, David Fuentes-Jimenez, Cristina Losada-Gutiérrez
As in many other different fields, deep learning has become the main approach in most computer vision applications, such as scene understanding, object recognition, computer-human…
3DFCNN: Real-Time Action Recognition using 3D Deep Neural Networks with Raw Depth Information
Adrian Sanchez-Caballero, Sergio de López-Diz, David Fuentes-Jimenez +4
Human actions recognition is a fundamental task in artificial vision, that has earned a great importance in recent years due to its multiple applications in different areas. %, suc…
DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera
David Fuentes-Jimenez, Roberto Martin-Lopez, Cristina Losada-Gutierrez +4
In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, i…
Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image
David Fuentes-Jimenez, David Casillas-Perez, Daniel Pizarro +2
We present Deep Shape-from-Template (DeepSfT), a novel Deep Neural Network (DNN) method for solving real-time automatic registration and 3D reconstruction of a deformable object vi…