Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory ( LSTM ) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention ( ST-Attention ) model, which studies spatial and temporal affinities jointly. Specifically, we introduce an attention mechanism to extract temporal affinity, learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

中文翻译：

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人体轨迹预测的时空注意模型

人体轨迹预测在许多相关应用中至关重要且很有前途。由于人类行为的不确定性，这极具挑战性，人类行为的不确定性不仅会受到他自己的影响，还会受到周围环境的影响。基于长期短期记忆（LSTM）模型的最新工作对轨迹预测任务进行了巨大改进。但是，它们大多数关注人类的空间影响，却忽略了时间影响。在本文中，我们提出了一种新颖的时空注意（ST-Attention）模型，该模型可以共同研究时空亲和力。具体来说，我们引入了一种注意力机制来提取时间亲和力，以了解不同时刻历史轨迹信息的重要性。要探索空间亲和力，一个深层的神经网络被用来衡量邻居的不同重要性。实验结果表明，与公开数据集上的最新方法相比，我们的方法具有竞争优势。