Next Article in Journal
Thermal-Drones as a Safe and Reliable Method for Detecting Subterranean Peat Fires
Previous Article in Journal
Advances in Drone Communications, State-of-the-Art and Architectures
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessFeature PaperArticle
Drones 2019, 3(1), 22; https://doi.org/10.3390/drones3010022

Vision-Based Indoor Scene Recognition from Time-Series Aerial Images Obtained Using a MAV Mounted Monocular Camera

Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo City, Akita 015–0055, Japan
*
Author to whom correspondence should be addressed.
Received: 15 December 2018 / Revised: 26 January 2019 / Accepted: 20 February 2019 / Published: 25 February 2019
  |  
PDF [2458 KB, uploaded 25 February 2019]
  |  

Abstract

This paper presents a vision-based indoor scene recognition method from aerial time-series images obtained using a micro air vehicle (MAV). The proposed method comprises two procedures: a codebook feature description procedure, and a recognition procedure using category maps. For the former procedure, codebooks are created automatically as visual words using self-organizing maps (SOMs) after extracting part-based local features using a part-based descriptor from time-series scene images. For the latter procedure, category maps are created using counter propagation networks (CPNs) with the extraction of category boundaries using a unified distance matrix (U-Matrix). Using category maps, topologies of image features are mapped into a low-dimensional space based on competitive and neighborhood learning. We obtained aerial time-series image datasets of five sets for two flight routes: a round flight route and a zigzag flight route. The experimentally obtained results with leave-one-out cross-validation (LOOCV) revealed respective mean recognition accuracies for the round flight datasets (RFDs) and zigzag flight datasets (ZFDs) of 71.7% and 65.5% for 10 zones. The category maps addressed the complexity of scenes because of segmented categories. Although extraction results of category boundaries using U-Matrix were partially discontinuous, we obtained comprehensive category boundaries that segment scenes into several categories. View Full-Text
Keywords: category maps; counter propagation networks; leave-one-out cross-validation; micro air vehicles; self-organizing maps; unified distance matrix category maps; counter propagation networks; leave-one-out cross-validation; micro air vehicles; self-organizing maps; unified distance matrix
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Madokoro, H.; Sato, K.; Shimoi, N. Vision-Based Indoor Scene Recognition from Time-Series Aerial Images Obtained Using a MAV Mounted Monocular Camera. Drones 2019, 3, 22.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Drones EISSN 2504-446X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top