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Egocentric visual scene description based on human-object interaction and deep spatial relations among objects

  • Gulraiz Khan
  • Muhammad Usman Ghani
  • Aiman Siddiqi
  • Zahoor-ur-Rehman
  • Sanghyun Seo
  • Sung Wook Baik
  • Irfan Mehmood
Article

Abstract

Visual Scene interpretation is one of the major areas of research in the recent past. Recognition of human object interaction is a fundamental step towards understanding visual scenes. Videos can be described via a variety of human-object interaction scenarios such as when both human and object are static (static-static), one is static while other is dynamic (static-dynamic) and both are dynamic (dynamic-dynamic). This paper presents a unified framework for the explanation of these interactions between humans and a variety of objects using deep learning as a pivot methodology. Human-object interaction is extracted through native machine learning techniques, while spatial relations are captured by training a model through convolution neural network. We also address the recognition of human posture in detail to provide egocentric visual description. After extracting visual features, sequential minimal optimization is employed for training our model. Extracted inter-action, spatial relations and posture information are fed into natural language generation module along with interacting object label to generate scene understanding. Evaluation of the proposed framework is done for two state of the art datasets i.e., MSCOCO and MSR3D Daily activity dataset; where achieved results are 78 and 91.16% accurate, respectively.

Keywords

Scene description Classification Surveillance Human-object interaction Spatial relations Deep neural network 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIP) (No. 2016R1A2B4011712) & by IGNITE, National Technology Fund, Pakistan for the project entitle “Automatic Surveillance System for Video Sequences”.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Gulraiz Khan
    • 1
  • Muhammad Usman Ghani
    • 1
    • 2
  • Aiman Siddiqi
    • 1
  • Zahoor-ur-Rehman
    • 3
  • Sanghyun Seo
    • 4
  • Sung Wook Baik
    • 5
  • Irfan Mehmood
    • 5
  1. 1.Al-Khwarizmi Institute of Computer Science UETLahorePakistan
  2. 2.Department of Computer Science and EngineeringUniversity of Engineering and Technology LahoreLahorePakistan
  3. 3.Department of Computer ScienceCOMSATS University IslamabadAttock CampusPakistan
  4. 4.Department of Media SoftwareSungkyul UniversityAnyang-siSouth Korea
  5. 5.Department of SoftwareSejong UniversitySeoulSouth Korea

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