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Detecting Hands in Egocentric Videos: Towards Action Recognition

  • Alejandro CartasEmail author
  • Mariella Dimiccoli
  • Petia Radeva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10672)

Abstract

Recently, there has been a growing interest in analyzing human daily activities from data collected by wearable cameras. Since the hands are involved in a vast set of daily tasks, detecting hands in egocentric images is an important step towards the recognition of a variety of egocentric actions. However, besides extreme illumination changes in egocentric images, hand detection is not a trivial task because of the intrinsic large variability of hand appearance. We propose a hand detector that exploits skin modeling for fast hand proposal generation and Convolutional Neural Networks for hand recognition. We tested our method on UNIGE-HANDS dataset and we showed that the proposed approach achieves competitive hand detection results.

Keywords

Ego-centric vision First person vision Hand-detection 

Notes

Acknowledgments

A.C. was supported by a doctoral fellowship from the Mexican Council of Science and Technology (CONACYT) (grant-no. 366596). This work was partially founded by TIN2015-66951-C2, SGR 1219, CERCA, ICREA Academia’2014 and 20141510 (Marató TV3). M.D. is grateful to the NVIDIA donation program for its support with a GPU card.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Alejandro Cartas
    • 1
    Email author
  • Mariella Dimiccoli
    • 1
    • 2
  • Petia Radeva
    • 1
    • 2
  1. 1.University of BarcelonaBarcelonaSpain
  2. 2.Computer Vision CentreCerdanyola del Valls, BarcelonaSpain

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