Pedestrian Crossing Intention Prediction at Red-Light Using Pose Estimation

Abstract

Pedestrians’ red-light crossing can present a threat to traffic safety. Among all the existing work related to pedestrian’s red-light crossing, there are few studies using trajectory data in time sequence. This paper uses pose estimation (keypoint detection) to generate pedestrians’ variables from CCTV videos. Four machine learning models are used to predict pedestrians’ crossing intention at intersections’ red-light. The best model achieves an accuracy of 0.920 and AUC value of 0.849, with data from three intersections. Different prediction horizons (up to 4 sec) are used. With longer prediction horizons, the sample size gets smaller, which partially leads to worse model performance. However, the performance with prediction horizon up to 2 sec is still good (AUC value as 0.841). It is found that keypoint variables such as the angles between ankle and knee (left side) and elbow and shoulder (right side) are important. This model can be further implemented in the Infrastructure-to-Vehicle (I2V) applications and thus prevent accidents due to pedestrians’ red-light crossing by issuing warnings to drivers.