Abstract Trajectory prediction is a crucial element of many automated tasks, such as autonomous navigation or video surveillance. To automatically predict the motion of an agent (e.g., pedestrian or car), the model needs to efficiently represent human motion and “understand” the external stimuli that may influence human behavior. In this work we propose a methodology to model the motion of agents in a video scene. Our method is based on space-varying sparse motion fields, which simultaneously characterize diverse motion patterns in the scene and implicitly learn contextual cues about the static environment, namely obstacles and semantic constraints. The sparse motion fields are applied to the task of long-term trajectory prediction using a probabilistic generative approach. Several benchmark data sets are used to demonstrate the potential of the proposed approach and show that our method achieves competitive state-of-the-art performances.

中文翻译：

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用于轨迹预测的稀疏运动场

摘要 轨迹预测是许多自动化任务的关键要素，例如自主导航或视频监控。为了自动预测代理（例如，行人或汽车）的运动，模型需要有效地表示人类运动并“理解”可能影响人类行为的外部刺激。在这项工作中，我们提出了一种方法来模拟视频场景中代理的运动。我们的方法基于空间变化的稀疏运动场，它同时表征场景中的不同运动模式，并隐式地学习关于静态环境的上下文线索，即障碍物和语义约束。使用概率生成方法将稀疏运动场应用于长期轨迹预测的任务。