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Computer Vision and the Internet of Things Ecosystem in the Connected Home

  • Carlos Lopez-CastañoEmail author
  • Carlos Ferrin-Bolaños
  • Luis Castillo-Ossa
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 800)

Abstract

An automatic food replenishment system for fridges may help people with cognitive and motor impairments to have a constant food supply at home. More even, sane people may benefit from this system because it is difficult to know accurately and precisely which goods are present in the fridge every day. This system has been a wish and a major challenge for both white good companies and food distributors for decades. It is known that this system requires two things: a sensing module for food stock tracking and another actuating module for food replenishment. The last module can be easily addressed since nowadays there exist many smartphone applications for food delivering, in fact, many food distributors allow their end-users to schedule food replenishment. On the contrary, food stock tracking is not that easy since this requires artificial intelligence to determine not only the different type of goods present in the fridge but also their quantity and quality. In this work, we address the problem of food detection in the fridge by a supervised computer vision algorithm based on Fast Region-based Convolutional Network and an internet of things ecosystem architecture in the connected home for getting high performance on training and deployment of the proposed method. We have tested our method on a data set of images containing sixteen types of goods in the fridge, built with the aid of a fridge-cam. Preliminary results suggest that it is possible to detect different goods in the fridge with good accuracy and that our method may rapidly scale.

Keywords

Computer vision Connected home Ecosystem Internet of things Machine learning 

Notes

Acknowledgments

This is a working progress being part of a research project (Computational Prototype for IoT Environments) among Colombia Institutions: Universidad Nacional, Universidad de Caldas and MABE, with code 36715.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Facultad de Ingeniería Y ArquitecturaUniversidad Nacional de ColombiaManizalesColombia
  2. 2.Universidad del ValleCaliColombia
  3. 3.GITIR Grupo Investigación Tecnologías Información y RedesUniversidad de CaldasManizalesColombia

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