Paper Reading: Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
Published:
1. Introduction
This paper presents a multi-task learning framework for pedestrian crossing action recognition and trajectory prediction, which utilizes 3D human keypoints extracted from raw sensor data to capture rich information on human pose and activity. Apply two auxiliary tasks and contrastive learning to enable auxiliary supervisions to improve the learned keypoints representation, which further enhances the performance of major tasks.