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Dataset: Pose Estimation

Publication: 
An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System
Authors: 
Sianna Puente, Cindy Madrid, Miguel Realpe, Boris X. Vintimilla.
Abstract: 

In  this  paper  we  discuss  the  feasibility  of  using  a  DCNN  to implement  Fall  and  Head  detection  for  a  Danger  Management System.  For  this  propose,  AlexNet and  Inception - v3  models  in MatConvNet and TensorFlow libraries were used for training new DCNNs  with  Fine - Tuning  method. Additionally,  a  new  public dataset was created, which includes diverse fall poses, as well as, top views of people walking in a scene.

Dataset description: 

The presented dataset has a total of 155,530 images. These images were obtained through the recording of members of CIDIS, in 4 sessions. In total, 10 videos with a duration of 4 minutes each were obtained. The participants were asked to bring different clothes, in order to give variety to the images. After this, the frames of the videos were separated at a rate of 5 frames per second. All these images were captured from a top view perspective. The original images have a resolution of 1280x720 pixels. Subsequently, a classification was made to decide which images could be used for the training. All these images were captured from a top view perspective.

For more information about the dataset, go to Readme.txt

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Part 1 - 1.1 GB Part 2 - 1.1 GB Part 3 - 1.1 GB Part 4 - 1.1 GB Part 5 - 1.1 GB Part 6 - 519 MB
Bibtex: 

@article{,
title = "An Empirical Comparison of DCNN libraries to implement the Vision Module of a Danger Management System",
journal = "International Conference on Deep Learning Technologies (ICDLT 2017)",
pages = "60-65",
year = "2017",
isbn = "978-1-4503-5232-1",
doi = "10.1145/3094243.3094255",
url = "http://refbase.cidis.espol.edu.ec/files/siannapuente/2017/56_SiannaPuente_etal2017.pdf",
author = "Sianna Puente, Cindy Madrid, Miguel Realpe, Boris X. Vintimilla",
keywords = "DCNN, Fall  detection, Head  detection, Tensorflow, Matconvnet, Danger Management Systems",
}