Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)
Source : 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (2018)
Keywords : Classification algorithms, Estimation, face, Face Classification, Face detection, Flood Management, floods, Machine vision, Semantic segmentation, TensorFlow.
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Wireless Networks and Applications (AWNA)
Year : 2018
Abstract : In the recent years, natural disasters have become frequent due to many causes such as global warming. Flooding is one of the most common natural disasters. In this work, we introduce a flood monitoring system based on Computer Vision. The system determines the depth of the water from images captured using smartphones. The average human height is used as reference to estimate the water level. The human face is classified based on gender and age group so that the average human height of the corresponding category can be used in estimation of water depth. The data set for the system is preprocessed to mitigate the effect of poor lighting conditions, occlusions and alignment. The ground-truth validation is done using images with known water depth to determine the accuracy of the system developed.
Cite this Research Publication : P. Vallimeena, Nair, B. B., and Rao, S. N., “Machine Vision Based Flood Depth Estimation Using Crowdsourced Images of Humans”, in 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2018.