Unsupervised Pedestrian Pose Prediction – A deep predictive coding network based approach for autonomous vehicle perception

Abstract

Pedestrian pose prediction is an important topic, related closely to robotics and automation. Accurate predictions of human poses and motion can facilitate a more thorough understanding and analysis of human behavior, which benefits real-world applications such as human-robot interaction, humanoid and bipedal robot design, and safe navigation of mobile robots and autonomous vehicles. This article describes a deep predictive coding network (PredNet)-based approach for unsupervised pedestrian pose prediction from 2D camera imagery and provides experimental results of two real-world autonomous vehicle data sets. The article also discusses topics for future work in unsupervised and semisupervised pedestrian pose prediction and its potential applications in robotics and automation systems.