通过动态聚类加速密集人群轨迹预测，提升效率且不损失精度。
Efficient Dense Crowd Trajectory Prediction Via Dynamic Clustering
- 基于相似属性动态聚类行人，用簇中心替代个体输入。
- 相比现有方法提速明显，内存占用降低，准确率保持不变。
- 适合需要实时处理的密集人群场景，如安防监控与大型活动管理。
人群轨迹预测在公共安全与管理中至关重要，可预防踩踏等灾难。现有方法通常基于人工标注数据预测个体轨迹并考虑周边物体，但在密集人群场景下表现不佳，因追踪输出存在大规模、噪声大、不准确等问题，导致计算成本高。为此，我们提出并全面评估一种基于聚类的新方法，通过时间上相似属性对行人进行分组，实现更高效的群体摘要。该即插即用方法可与现有轨迹预测器结合，以我们的簇中心替代其行人输入。我们在多个具有挑战性的密集人群场景上进行了评估，结果表明，相比当前最优方法，本方法在保持精度的同时显著提升处理速度并降低内存使用。
原文摘要 · Abstract (English)
Crowd trajectory prediction plays a crucial role in public safety and management, where it can help prevent disasters such as stampedes. Recent works address the problem by predicting individual trajectories and considering surrounding objects based on manually annotated data. However, these approaches tend to overlook dense crowd scenarios, where the challenges of automation become more pronounced due to the massiveness, noisiness, and inaccuracy of the tracking outputs, resulting in high computational costs. To address these challenges, we propose and extensively evaluate a novel cluster-based approach that groups individuals based on similar attributes over time, enabling faster execution through accurate group summarisation. Our plug-and-play method can be combined with existing trajectory predictors by using our output centroid in place of their pedestrian input. We evaluate our proposed method on several challenging dense crowd scenes. We demonstrated that our approach leads to faster processing and lower memory usage when compared with state-of-the-art methods, while maintaining the accuracy
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