66 0

Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network

Title
Stochastic Remote Sensing Event Classification over Adaptive Posture Estimation via Multifused Data and Deep Belief Network
Author
김기범
Keywords
deep belief network; event classification; human body part detection; multifused data; pseudo-2D-stick model
Issue Date
2021-02
Publisher
MDPI
Citation
REMOTE SENSING, v. 13, No. 5, Article no. 912, 29pp
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. Multifused data that include energy, 3D Cartesian view, angular geometric, skeleton zigzag and moveable 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.
URI
https://www.mdpi.com/2072-4292/13/5/912https://repository.hanyang.ac.kr/handle/20.500.11754/166947
ISSN
2072-4292
DOI
10.3390/rs13050912
Appears in Collections:
ETC[S] > 연구정보
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE