Next Article in Journal
iCOR Atmospheric Correction on Sentinel-3/OLCI over Land: Intercomparison with AERONET, RadCalNet, and SYN Level-2
Previous Article in Journal
Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula
Open AccessArticle

A Method for Detection of Small Moving Objects in UAV Videos

1
Faculty of Electrical Engineering, University of Banja Luka, Patre 5, 78000 Banja Luka, Bosnia and Herzegovina
2
Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, 10000 Zagreb, Croatia
3
Department of Ecology, Agronomy and Aquaculture, University of Zadar, Trg Kneza Višeslava 9, 23000 Zadar, Croatia
4
Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia
*
Author to whom correspondence should be addressed.
Academic Editor: Lin Cao
Remote Sens. 2021, 13(4), 653; https://doi.org/10.3390/rs13040653
Received: 31 December 2020 / Revised: 2 February 2021 / Accepted: 5 February 2021 / Published: 11 February 2021
(This article belongs to the Section AI Remote Sensing)
Detection of small moving objects is an important research area with applications including monitoring of flying insects, studying their foraging behavior, using insect pollinators to monitor flowering and pollination of crops, surveillance of honeybee colonies, and tracking movement of honeybees. However, due to the lack of distinctive shape and textural details on small objects, direct application of modern object detection methods based on convolutional neural networks (CNNs) shows considerably lower performance. In this paper we propose a method for the detection of small moving objects in videos recorded using unmanned aerial vehicles equipped with standard video cameras. The main steps of the proposed method are video stabilization, background estimation and subtraction, frame segmentation using a CNN, and thresholding the segmented frame. However, for training a CNN it is required that a large labeled dataset is available. Manual labelling of small moving objects in videos is very difficult and time consuming, and such labeled datasets do not exist at the moment. To circumvent this problem, we propose training a CNN using synthetic videos generated by adding small blob-like objects to video sequences with real-world backgrounds. The experimental results on detection of flying honeybees show that by using a combination of classical computer vision techniques and CNNs, as well as synthetic training sets, the proposed approach overcomes the problems associated with direct application of CNNs to the given problem and achieves an average F1-score of 0.86 in tests on real-world videos. View Full-Text
Keywords: small object detection; training on synthetic videos; convolutional neural networks; UAVs; honeybees detection small object detection; training on synthetic videos; convolutional neural networks; UAVs; honeybees detection
Show Figures

Graphical abstract

MDPI and ACS Style

Stojnić, V.; Risojević, V.; Muštra, M.; Jovanović, V.; Filipi, J.; Kezić, N.; Babić, Z. A Method for Detection of Small Moving Objects in UAV Videos. Remote Sens. 2021, 13, 653. https://doi.org/10.3390/rs13040653

AMA Style

Stojnić V, Risojević V, Muštra M, Jovanović V, Filipi J, Kezić N, Babić Z. A Method for Detection of Small Moving Objects in UAV Videos. Remote Sensing. 2021; 13(4):653. https://doi.org/10.3390/rs13040653

Chicago/Turabian Style

Stojnić, Vladan; Risojević, Vladimir; Muštra, Mario; Jovanović, Vedran; Filipi, Janja; Kezić, Nikola; Babić, Zdenka. 2021. "A Method for Detection of Small Moving Objects in UAV Videos" Remote Sens. 13, no. 4: 653. https://doi.org/10.3390/rs13040653

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

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop