Learning Priors of Human Motion With Vision Transformers
This work addresses motion prediction for urban planning and robotics, but it is incremental as it adapts an existing method (ViT) to a specific domain.
The authors tackled the problem of understanding human motion patterns for applications like urban mobility and robot navigation by proposing a Vision Transformer (ViT) architecture, which improved metrics compared to a CNN-based method on a standard dataset.
A clear understanding of where humans move in a scenario, their usual paths and speeds, and where they stop, is very important for different applications, such as mobility studies in urban areas or robot navigation tasks within human-populated environments. We propose in this article, a neural architecture based on Vision Transformers (ViTs) to provide this information. This solution can arguably capture spatial correlations more effectively than Convolutional Neural Networks (CNNs). In the paper, we describe the methodology and proposed neural architecture and show the experiments' results with a standard dataset. We show that the proposed ViT architecture improves the metrics compared to a method based on a CNN.