Prediction of Dangerous Pedestrians using Depth and Stance Estimation

According to the Tokyo Fire Department, from 2015 to 2019, there were 211 people who were taken to the hospital due to accidents involving pedestrians using their smartphones while walking. Around 40% of these accidents were due to pedestrians running into bicycles, people, and other objects. Local ordinances have been passed to reduce the danger posed by pedestrians engaging in this unsafe behavior, but this has not significantly reduced the use of smartphones while walking. In this paper, we propose a metric for evaluating the level of danger posed by pedestrians captured on video. In our proposed method, we extract the skeletons of pedestrians using MediaPipe, then predict the danger level by estimating the pedestrian stance and depth within the image. Then, we evaluate the accuracy of our model by calculating the accuracy of the depth and stance estimators on a video taken by a car-mounted camera. We estimated danger levels on a local road and obtained an accuracy of 0.459.