Anticipating human motion in crowded scenarios is essential for developing intelligent transportation systems, social-aware robots and advanced video surveillance applications. A key component of this task is represented by the inherently multi-modal nature of human paths which makes socially acceptable multiple futures when human interactions are involved. To this end, we propose a generative architecture for multi-future trajectory predictions based on Conditional Variational Recurrent Neural Networks (C-VRNNs). Conditioning mainly relies on prior belief maps, representing most likely moving directions and forcing the model to consider past observed dynamics in generating future positions. Human interactions are modelled with a graph-based attention mechanism enabling an online attentive hidden state refinement of the recurrent estimation. To corroborate our model, we perform extensive experiments on publicly-available datasets (e.g., ETH/UCY, Stanford Drone Dataset, STATS SportVU NBA, Intersection Drone Dataset and TrajNet++) and demonstrate its effectiveness in crowded scenes compared to several state-of-the-art methods.

预测拥挤场景中的人体运动对于开发智能交通系统、具有社会意识的机器人和先进的视频监控应用至关重要。这项任务的一个关键组成部分是人类路径固有的多模式性质，当涉及人类互动时，这使得社会可以接受多种未来。为此，我们提出了一种基于条件变分循环神经网络（C-VRNN）的多未来轨迹预测的生成架构。调节主要依赖于先验信念图，代表最可能的移动方向并迫使模型在生成未来位置时考虑过去观察到的动态。人类交互使用基于图的注意力机制进行建模，从而实现对循环估计的在线注意力隐藏状态细化。