Version 1
: Received: 6 November 2018 / Approved: 7 November 2018 / Online: 7 November 2018 (09:41:39 CET)
How to cite:
Bhattarai, N.; Nakamura, T.; Mozumder, C. Real Time Human Detection and Localization Using Consumer Grade Camera and Commercial UAV. Preprints2018, 2018110156. https://doi.org/10.20944/preprints201811.0156.v1
Bhattarai, N.; Nakamura, T.; Mozumder, C. Real Time Human Detection and Localization Using Consumer Grade Camera and Commercial UAV. Preprints 2018, 2018110156. https://doi.org/10.20944/preprints201811.0156.v1
Bhattarai, N.; Nakamura, T.; Mozumder, C. Real Time Human Detection and Localization Using Consumer Grade Camera and Commercial UAV. Preprints2018, 2018110156. https://doi.org/10.20944/preprints201811.0156.v1
APA Style
Bhattarai, N., Nakamura, T., & Mozumder, C. (2018). Real Time Human Detection and Localization Using Consumer Grade Camera and Commercial UAV. Preprints. https://doi.org/10.20944/preprints201811.0156.v1
Chicago/Turabian Style
Bhattarai, N., Tai Nakamura and Chitrini Mozumder. 2018 "Real Time Human Detection and Localization Using Consumer Grade Camera and Commercial UAV" Preprints. https://doi.org/10.20944/preprints201811.0156.v1
Abstract
Human detection from Unmanned Aerial Vehicles (UAV) is gaining popularity in the field of disaster management, crowd counting, people monitoring. Real time human detection from UAV is a challenging task, because of many constraints involved. This study proposes a system for real time detection of humans on videos captured from UAVs addressing three of these constraints namely, flying height, computation time and scale of viewing. The proposed method integrated an android application with a binary classifier based on Haar-features to automatically detect human / non-human class from UAV images. The video frames were parsed and detected humans from image frames were geo-localized and visualized on Google Earth. The performance was evaluated for geo-localization accuracy, computation time and detection accuracy, considering human coverage – pixel size relationship for various heights and scale factor. Based on flying height - human size relationship and tradeoff between detection accuracy vs computation time, the study came up with optimal parameters for OpenCV’s cv2.cascadeClassifier. detectMultiScale function. This paper establishes a strong ground for further research relating to real time human detection from UAV.
Keywords
Unmanned Aerial Vehicle (UAV), Haar-like features, real time, Geographic Information Systems (GIS), human detection, geolocation error, OpenCV
Subject
Computer Science and Mathematics, Information Systems
Copyright:
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.
