Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction

• URL: http://arxiv.org/abs/2412.02395v1
• Date: Tue, 03 Dec 2024 11:47:33 GMT
• Title: Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction
• Authors: Ziqian Zou, Conghao Wong, Beihao Xia, Qinmu Peng, Xinge You,
• Abstract summary: This work proposes the GrouP ConCeption (short for GPCC) model composed of the Group method and the Conception module. The GPCC model demonstrates significant improvements in trajectory prediction accuracy, validating its effectiveness in modeling both social and individual dynamics.
• Score: 14.009392073139441
• License:
• 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 acoustic information surrounding the target agent. Evaluated across multiple datasets, the GPCC model demonstrates significant improvements in trajectory prediction accuracy, validating its effectiveness in modeling both social and individual dynamics. The qualitative analysis also indicates that the GPCC framework successfully leverages grouping and perception cues human-like intuitively to validate the proposed model's explainability in pedestrian trajectory forecasting.

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