Real-time flood detection for video surveillance
Cáceres Hernández, Danilo
Hyun Jo, Kang
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This paper introduces the real-time flash flood detection method for stationary surveillance cameras. It can be applied for rural and urban areas and capable of working during day time. The background subtraction was used to detect all changes appear in a scene. After this step, many pixel belonging to the same moving objects may be divided. They are united by morphological closing. Too small separate objects are then removed form the scene. Color probability was calculated for all the pixels belonging to a foreground mask and connected components with low probability value were filtered out. Finally, results were improved by edge density and boundary roughness. The most time consuming step was implemented in parallel using CUDA. Real-time performance was achieved in this way. The algorithm was tested on publicly accepted video.