Social group detection is a crucial aspect of various robotic applications,
including robot navigation and human-robot interactions. To date, a range of
model-based techniques have been employed to address this challenge, such as
the F-formation and trajectory similarity frameworks. However, these approaches
often fail to provide reliable results in crowded and dynamic scenarios. Recent
advancements in this area have mainly focused on learning-based methods, such
as deep neural networks that use visual content or human pose. Although visual
content-based methods have demonstrated promising performance on large-scale
datasets, their computational complexity poses a significant barrier to their
practical use in real-time applications. To address these issues, we propose a
simple and efficient framework for social group detection. Our approach
explores the impact of motion trajectory on social grouping and utilizes a
novel, reliable, and fast data-driven method. We formulate the individuals in a
scene as a graph, where the nodes are represented by LSTM-encoded trajectories
and the edges are defined by the distances between each pair of tracks. Our
framework employs a modified graph transformer module and graph clustering
losses to detect social groups. Our experiments on the popular JRDBAct dataset
reveal noticeable improvements in performance, with relative improvements
ranging from 2% to 11%. Furthermore, our framework is significantly faster,
with up to 12x faster inference times compared to state-of-the-art methods
under the same computation resources. These results demonstrate that our
proposed method is suitable for real-time robotic applications.

我们提出了一种用于社交群体检测的简单高效的图形转换模块和图形聚类损失，能够在人群拥挤动态的情况下实现较高的表现。与先前基于视觉内容的方法相比，在保持准确性的同时，实现了高速的推理时间。该方法适用于实时的机器人应用。