Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

Part of the Studies in Systems, Decision and Control book series (SSDC, volume 273)


Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.


Deep learning Fall detection system CNN Multiple cameras 


Conflict of interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Funding Statement

The authors declare that this work was performed as part of their employment in Universidad Panamericana (Mexico).


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Authors and Affiliations

  1. 1.Facultad de IngenieríaUniversidad PanamericanaAguascalientesMexico
  2. 2.Facultad de IngenieríaUniversidad PanamericanaCiudad de MéxicoMexico

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