Abstract
Pedestrians have an intuitive ability for navigation to avoid obstacles and nearby pedestrians. If we want to predict future positions of a pedestrian, we should know how the pedestrian adjust his direction to avoid collisions. In this work, we present a simple and effective framework for human trajectory prediction to generate the future sequence based on pedestrian past positions. The method, called Collision-Free LSTM, extends the classical LSTM by adding Repulsion pooling layer to share hidden-states of neighboring pedestrians. The model can learn both the temporal information of trajectories and the interactions between pedestrians, which is in contrast to traditional methods using hand-crafted features such as Social forces. The experiments results on two public datasets show that our model can achieve state-of-the-art performance with assessment metrics.
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Xu, K., Qin, Z., Wang, G., Huang, K., Ye, S., Zhang, H. (2018). Collision-Free LSTM for Human Trajectory Prediction. In: Schoeffmann, K., et al. MultiMedia Modeling. MMM 2018. Lecture Notes in Computer Science(), vol 10704. Springer, Cham. https://doi.org/10.1007/978-3-319-73603-7_9
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