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首页> 外文会议>The 1st International Conference onArtificial Intelligence in Information and Communication >Deep Learning Based Pedestrian Trajectory Prediction Considering Location Relationship between Pedestrians
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Deep Learning Based Pedestrian Trajectory Prediction Considering Location Relationship between Pedestrians

机译：考虑行人之间位置关系的基于深度学习的行人轨迹预测

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摘要

Pedestrian trajectory prediction is a challenging task because of the complex nature of humans. We propose to predict displacement between neighboring frames for each pedestrian sequentially. Specifically, we use an LSTM to model motion information for all pedestrians and use a mlp to map the location of each pedestrian to a high dimensional feature space where the inner product between features is used as a measurement for the positional relationship between two pedestrians. Then we weight the motion features of all pedestrians based on their positional relationship to the target for location displacement prediction. Experiments on publicly available datasets validate the effectiveness of our method for trajectory prediction.
机译：由于人类的复杂性，行人轨迹预测是一项具有挑战性的任务。我们建议依次预测每个行人在相邻帧之间的位移。具体来说，我们使用LSTM对所有行人的运动信息进行建模，并使用mlp将每个行人的位置映射到高维特征空间，其中特征之间的内积用作两个行人之间位置关系的度量。然后，我们根据所有行人与目标的位置关系对所有行人的运动特征进行加权，以进行位置位移预测。在公开数据集上进行的实验验证了我们的轨迹预测方法的有效性。

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