论文标题
人类轨迹预测的相互学习网络
Reciprocal Learning Networks for Human Trajectory Prediction
论文作者
论文摘要
我们观察到，人类轨迹不仅可以预测，而且是可预测的。前进和向后轨迹都遵循相同的社会规范，并遵守相同的身体约束，其时间方向的唯一差异。基于这个独特的属性，我们开发了一种用于人类轨迹预测的新方法，称为相互学习。两个网络（向后和向后的预测网络都紧密耦合，满足了相互的约束，这使它们可以共同学习。基于此约束，我们借用了深神经网络的对抗性攻击的概念，该概念迭代地修改了网络的输入以匹配给定或强制网络输出，并为网络预测开发了一种新方法，称为相对于预测的相互攻击。它进一步提高了预测准确性。我们在基准数据集上的实验结果表明，我们的新方法优于人类轨迹预测的最新方法。
We observe that the human trajectory is not only forward predictable, but also backward predictable. Both forward and backward trajectories follow the same social norms and obey the same physical constraints with the only difference in their time directions. Based on this unique property, we develop a new approach, called reciprocal learning, for human trajectory prediction. Two networks, forward and backward prediction networks, are tightly coupled, satisfying the reciprocal constraint, which allows them to be jointly learned. Based on this constraint, we borrow the concept of adversarial attacks of deep neural networks, which iteratively modifies the input of the network to match the given or forced network output, and develop a new method for network prediction, called reciprocal attack for matched prediction. It further improves the prediction accuracy. Our experimental results on benchmark datasets demonstrate that our new method outperforms the state-of-the-art methods for human trajectory prediction.
