Vision Transformerによる人間の移動事前分布の学習
Learning Priors of Human Motion With Vision Transformers
Vision Transformerを用いて、人がどのように移動しどこで止まるかの事前分布を学習する手法を提案し、CNNベースの手法より精度が向上することを示した。
著者: Placido Falqueto, Alberto Sanfeliu, Luigi Palopoli, Daniele Fontanelli
分類: cs.CV, cs.RO
原文アブストラクト
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.
