A flood detection and warning system based on video content analysis
College
College of Computer Studies
Department/Unit
Software Technology
Document Type
Conference Proceeding
Source Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
10073 LNCS
First Page
65
Last Page
74
Publication Date
1-1-2016
Abstract
Floods are becoming more frequent and extreme due to climate change. Early detection is critical in providing a timely response to prevent damage to property and life. Previous methods for flood detection make use of specialized sensors or satellite imagery. In this paper, we propose a method for event detection based on video content analysis of feeds from surveillance cameras, which have become more common and readily available. Since these cameras are static, we can use image masks to identify regions of interest in the video where the flood would likely occur. We then perform background subtraction and then use image segmentation on the foreground region. The main features of the segment that we use to identify if it is a flooded region are: color, size and edge density. We use a probabilistic model of the color of the flood based on our set of collected flood images. We determine the size of the segment relative to the frame size as another indicator that it is flood since flooded regions tend to occupy a huge region of the frame. Finally, we perform a form of ripple detection by performing edge detection and using the edge density as a possible indicator for ripples and consequently flood. We then broadcast an SMS message after detecting a flood event consistently across multiple frames for a specified time period. Our results show that this simple technique can adequately detect floods in real-time. © Springer International Publishing AG 2016.
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Digitial Object Identifier (DOI)
10.1007/978-3-319-50832-0_7
Recommended Citation
San Miguel, M. P., & Ruiz, C. R. (2016). A flood detection and warning system based on video content analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10073 LNCS, 65-74. https://doi.org/10.1007/978-3-319-50832-0_7
Disciplines
Computer Sciences
Keywords
Flood warning systems; Image segmentation; Climatic changes—Detection
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