论文标题
注意运动模式：带有新型卷积轨道轨迹预测的新型卷积操作员的时间CNN
Noticing Motion Patterns: Temporal CNN with a Novel Convolution Operator for Human Trajectory Prediction
论文作者
论文摘要
我们提出了一种基于卷积神经网络的方法，用于在顺序轨迹数据中学习，检测和提取模式，此处称为社会模式提取卷积（Social-PEC）。在人类轨迹预测问题上进行的一组实验表明，在某些情况下，我们的模型与艺术的状态和表现相当。更重要的是，所提出的方法揭示了以前使用合并层的模糊性，这是一种直观地解释决策过程的方法。
We propose a Convolutional Neural Network-based approach to learn, detect,and extract patterns in sequential trajectory data, known here as Social Pattern Extraction Convolution (Social-PEC). A set of experiments carried out on the human trajectory prediction problem shows that our model performs comparably to the state of the art and outperforms in some cases. More importantly,the proposed approach unveils the obscurity in the previous use of pooling layer, presenting a way to intuitively explain the decision-making process.
