Understanding human-contextual interaction to predict human trajectories is a challenging problem. Most of previous trajectory prediction approaches focused on modeling the human-human interaction located in a near neighborhood and neglected the influence of individuals which are farther in the scene as well as the scene layout. To alleviate these limitations, in this article we propose a model to address pedestrian trajectory prediction using a latent variable model aware of the human-contextual interaction. Our proposal relies on contextual information that influences the trajectory of pedestrians to encode human-contextual interaction. We model the uncertainty about future trajectories via latent variational model and captures relative interpersonal influences among all the subjects within the scene and their interaction with the scene layout to decode their trajectories. In extensive experiments, on publicly available datasets, it is shown that using contextual information and latent variational model, our trajectory prediction model achieves competitive results compared to state-of-the-art models.

中文翻译：

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基于潜在变分模型的情境感知人类轨迹预测

了解人与人之间的相互作用以预测人的轨迹是一个具有挑战性的问题。先前的大多数轨迹预测方法都集中于对位于附近社区的人与人之间的交互进行建模，而忽略了场景和场景布局中更远的个体的影响。为了缓解这些局限性，在本文中，我们提出了一个模型，该模型使用了解人与上下文交互作用的潜在变量模型来处理行人轨迹预测。我们的建议依赖于影响行人轨迹的上下文信息来对人与上下文之间的交互进行编码。我们通过潜在的变分模型对未来轨迹的不确定性进行建模，并捕获场景内所有主体之间的相对人际关系及其与场景布局的交互作用，以解码其轨迹。在广泛的实验中，在可公开获得的数据集上，表明与现有技术模型相比，使用上下文信息和潜在变异模型，我们的轨迹预测模型可实现竞争性结果。