Identification of pedestrian submerged parts in urban flooding based on images and deep learning

Abstract

During urban flooding, pedestrians are often trapped in floodwater, and some pedestrians even fall or drown. The pedestrian submerged part (i.e., the human body part that water surface reaches) is an important reference indicator for judging dangerous situation of pedestrians. Flood images usually contain the information about pedestrian submerged parts. We proposed an automated method for identifying pedestrian submerged parts from images. This method utilizes relevant deep learning technologies to segment water surfaces, detect the pedestrians in floodwater, and detect the human keypoints of the pedestrians from images, and then identify submerged parts of the pedestrians according to the relationship between the human keypoints and the water surfaces. This method achieves an accuracy of 90.71% in identifying pedestrian submerged parts on an image dataset constructed from Internet images. The result shows that this method could effectively identify pedestrian submerged parts from images with high accuracy.