Stochastic remote sensing event classification over adaptive posture estimation via multifused data and deep belief network

Munkhjargal Gochoo, Israr Akhter, Ahmad Jalal, Kibum Kim

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Advances in video capturing devices enable adaptive posture estimation (APE) and event classification of multiple human-based videos for smart systems. Accurate event classification and adaptive posture estimation are still challenging domains, although researchers work hard to find solutions. In this research article, we propose a novel method to classify stochastic remote sensing events and to perform adaptive posture estimation. We performed human silhouette extraction using the Gaussian Mixture Model (GMM) and saliency map. After that, we performed human body part detection and used a unified pseudo-2D stick model for adaptive posture estimation. Mul-tifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and move-able body parts were applied. Using a charged system search, we optimized our feature vector and deep belief network. We classified complex events, which were performed over sports videos in the wild (SVW), Olympic sports, UCF aerial action dataset and UT-interaction datasets. The mean accuracy of human body part detection was 83.57% over the UT-interaction, 83.00% for the Olympic sports and 83.78% for the SVW dataset. The mean event classification accuracy was 91.67% over the UT-interaction, 92.50% for Olympic sports and 89.47% for SVW dataset. These results are superior compared to existing state-of-the-art methods.

Original languageEnglish
Article number912
Pages (from-to)1-29
Number of pages29
JournalRemote Sensing
Volume13
Issue number5
DOIs
Publication statusPublished - Mar 1 2021

Keywords

  • Deep belief network
  • Event classification
  • Human body part detection
  • Multifused data
  • Pseudo-2D-stick model

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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