Group Split and Merge Prediction With 3D Convolutional Networks

Abstract

Mobile robots in crowds often have limited navigation capability due to insufficient evaluation of pedestrian behavior. We strengthen this capability by predicting splits and merges in multi-person groups. Successful predictions should lead to more efficient planning while also increasing human acceptance of robot behavior. We take a novel approach by formulating this as a video prediction problem, where group splits or merges are predicted given a history of geometric social group shape transformations. We take inspiration from the success of 3D convolution models for video-related tasks. By treating the temporal dimension as a spatial dimension, a modified C3D model successfully captures the temporal features required to perform the prediction task. We demonstrate performance on several datasets and analyze transfer ability to other settings. While current approaches for tracking human motion are not explicitly designed for this task, our approach performs significantly better at predicting the occurrence of splits and merges. We also draw human interpretations from the model's learned features.