Motion Descriptor for Human Gesture Recognition in Low Resolution Images

  • António Ferreira
  • Guilherme Silva
  • André Dias
  • Alfredo Martins
  • Aurélio Campilho
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 417)

Abstract

A great variety of human gesture recognition methods exist in the literature, yet there is still a lack of solutions to encompass some of the challenges imposed by real life scenarios. In this document, a gesture recognition for robotic search and rescue missions in the high seas is presented. The method aims to identify shipwrecked people by recognizing the hand waving gesture sign.

We introduce a novel motion descriptor, through which high recognition accuracy can be achieved even for low resolution images. The method can be simultaneously applied to rigid object characterization, hence object and gesture recognition can be performed simultaneously.

The descriptor has a simple implementation and is invariant to scale and gesture speed. Tests, preformed on a maritime dataset of thermal images, proved the descriptor ability to reach a meaningful representation for very low resolution objects. Recognition rates with 96.3% of accuracy were achieved.

Keywords

Motion descriptor Gesture descriptor Search and rescue Robotics Gesture recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kaâniche, M., Bremond, F.: Recognizing Gestures by Learning Local Motion Signatures of HOG Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 34, 2247–2258 (2012)CrossRefGoogle Scholar
  2. 2.
    Lertniphonphan, K., Aramvith, S., Chalidabhongse, T.H.: Human action recognition using direction histograms of optical flow. In: 2011 11th International Symposium on Communications and Information Technologies (ISCIT), pp. 574–579, October 2011Google Scholar
  3. 3.
    Gorelick, L., Blank, M., Shechtman, E., Irani, M., Basri, R.: Actions as space-time shapes. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2247–2253 (2007)CrossRefGoogle Scholar
  4. 4.
    Laptev, I.: On space-time interest points. Int. J. Comput. Vision 64(2–3), 107–123 (2005)CrossRefGoogle Scholar
  5. 5.
    Bradski, G., Davis, J.: Motion segmentation and pose recognition with motion history gradients. In: Fifth IEEE Workshop on Applications of Computer Vision, pp. 238–244 (2000)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • António Ferreira
    • 1
  • Guilherme Silva
    • 1
  • André Dias
    • 1
  • Alfredo Martins
    • 1
  • Aurélio Campilho
    • 2
  1. 1.INESC TEC - INESC Technology and Science and ISEP/IPP - School of EngineeringPolytechnic Institute of PortoPortoPortugal
  2. 2.INESC TEC - INESC Technology and Science and FEUP - Faculty of EngineeringUniversity of PortoPortoPortugal

Personalised recommendations