2 research outputs found
Detecting floodwater on roadways from image data with handcrafted features and deep transfer learning
Detecting roadway segments inundated due to floodwater has important
applications for vehicle routing and traffic management decisions. This paper
proposes a set of algorithms to automatically detect floodwater that may be
present in an image captured by mobile phones or other types of optical
cameras. For this purpose, image classification and flood area segmentation
methods are developed. For the classification task, we used Local Binary
Patterns (LBP), Histogram of Oriented Gradients (HOG) and pre-trained deep
neural network (VGG-16) as feature extractors and trained logistic regression,
k-nearest neighbors, and decision tree classifiers on the extracted features.
Pre-trained VGG-16 network with logistic regression classifier outperformed all
other methods. For the flood area segmentation task, we investigated superpixel
based methods and Fully Convolutional Neural Network (FCN). Similar to the
classification task, we trained logistic regression and k-nearest neighbors
classifiers on the superpixel areas and compared that with an end-to-end
trained FCN. Conditional Random Fields (CRF) method was applied after both
segmentation methods to post-process coarse segmentation results. FCN offered
the highest scores in all metrics; it was followed by superpixel-based logistic
regression and then superpixel-based KNN.Comment: Accepted at IEEE-ITSC 2019: The 22nd IEEE International Conference on
Intelligent Transportation Systems, Auckland, NZ, October 27-30, 201
Review on Computer Vision Techniques in Emergency Situation
In emergency situations, actions that save lives and limit the impact of
hazards are crucial. In order to act, situational awareness is needed to decide
what to do. Geolocalized photos and video of the situations as they evolve can
be crucial in better understanding them and making decisions faster. Cameras
are almost everywhere these days, either in terms of smartphones, installed
CCTV cameras, UAVs or others. However, this poses challenges in big data and
information overflow. Moreover, most of the time there are no disasters at any
given location, so humans aiming to detect sudden situations may not be as
alert as needed at any point in time. Consequently, computer vision tools can
be an excellent decision support. The number of emergencies where computer
vision tools has been considered or used is very wide, and there is a great
overlap across related emergency research. Researchers tend to focus on
state-of-the-art systems that cover the same emergency as they are studying,
obviating important research in other fields. In order to unveil this overlap,
the survey is divided along four main axes: the types of emergencies that have
been studied in computer vision, the objective that the algorithms can address,
the type of hardware needed and the algorithms used. Therefore, this review
provides a broad overview of the progress of computer vision covering all sorts
of emergencies.Comment: 25 page