用深度学习实时识别行人集群，兼顾速度与准确率。
Real-Time Moving Flock Detection in Pedestrian Trajectories Using Sequential Deep Learning Models
- 分两阶段：先判别成对轨迹，再聚合识别多行人集群
- 在动态噪声环境中仍保持高精度与稳定性
- 可扩展识别队伍、蜂群等集体行为，适合智能交通场景
理解人群集体移动对人群管理、自主导航和人机交互至关重要。本文研究使用循环神经网络（RNN）、长短期记忆网络（LSTM）和Transformer等序列深度学习模型，实现多行人轨迹中的实时集群检测。所提方法包含两阶段流程：首先利用预训练的二分类模型进行成对轨迹分类，其次将学习到的表征用于动态识别多智能体集群。我们在真实世界群体移动数据集上验证方法，结果表明该模型在不同序列长度和多样运动模式下均表现出鲁棒性。实验显示，模型在动态且嘈杂环境中仍能持续以高精度和稳定性检测行人集群。此外，方法还可扩展至识别队伍、蜂群等其他集体运动形式，为更全面的多智能体行为分析提供可能。
原文摘要 · Abstract (English)
Understanding collective pedestrian movement is crucial for applications in crowd management, autonomous navigation, and human-robot interaction. This paper investigates the use of sequential deep learning models, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers, for real-time flock detection in multi-pedestrian trajectories. Our proposed approach consists of a two-stage process: first, a pre-trained binary classification model is used for pairwise trajectory classification, and second, the learned representations are applied to identify multi-agent flocks dynamically. We validate our method using real-world group movement datasets, demonstrating its robustness across varying sequence lengths and diverse movement patterns. Experimental results indicate that our model consistently detects pedestrian flocks with high accuracy and stability, even in dynamic and noisy environments. Furthermore, we extend our approach to identify other forms of collective motion, such as convoys and swarms, paving the way for more comprehensive multi-agent behavior analysis.
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