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.

中文翻译：

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基于图像和深度学习的城市洪水中行人淹没部位识别


在城市洪水期间，行人经常被困在洪水中，一些行人甚至跌倒或溺水。行人淹没部分（即水面到达的人体部分）是判断行人危险情况的重要参考指标。洪水图像通常包含有关行人淹没部分的信息。我们提出了一种从图像中识别行人被淹没部分的自动化方法。该方法利用相关的深度学习技术对水面进行分割，检测洪水中的行人，并从图像中检测行人的人体关键点，然后根据人体关键点与水面的关系识别行人的水下部分。该方法在由 Internet 图像构建的图像数据集上识别行人淹没部分的准确率达到 90.71%。结果表明，该方法能够从图像中有效识别行人淹没部分，且精度高。