Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction
Ziqian Zou, Conghao Wong, Beihao Xia, Qinmu Peng, Xinge You

TL;DR
This paper introduces the GPCC model that improves pedestrian trajectory prediction by perceiving social interactions through grouping and multimodal perception, inspired by human social perception, and demonstrates its effectiveness across multiple datasets.
Contribution
The paper proposes the GPCC model that combines grouping and perception modules to enhance trajectory prediction by modeling social interactions more human-like.
Findings
Significant improvements in trajectory prediction accuracy.
Effective modeling of social and individual dynamics.
Enhanced explainability through human-like perception cues.
Abstract
Understanding and anticipating human movement has become more critical and challenging in diverse applications such as autonomous driving and surveillance. The complex interactions brought by different relations between agents are a crucial reason that poses challenges to this task. Researchers have put much effort into designing a system using rule-based or data-based models to extract and validate the patterns between pedestrian trajectories and these interactions, which has not been adequately addressed yet. Inspired by how humans perceive social interactions with different level of relations to themself, this work proposes the GrouP ConCeption (short for GPCC) model composed of the Group method, which categorizes nearby agents into either group members or non-group members based on a long-term distance kernel function, and the Conception module, which perceives both visual and…
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Taxonomy
TopicsTraffic and Road Safety · Autonomous Vehicle Technology and Safety · Video Surveillance and Tracking Methods
