Sparse Pedestrian Character Learning for Trajectory Prediction
Yonghao Dong Le Wang Sanpin Zhou Gang Hua Changyin Sun
Pedestrian trajectory prediction in a first-person view has recently
attracted much attention due to its importance in autonomous driving. Recent
work utilizes pedestrian character information, \textit{i.e.}, action and
appearance, to improve the learned trajectory embedding and achieves
state-of-the-art performance. However, it neglects the invalid and negative
pedestrian character information, which is harmful to trajectory representation
and thus leads to performance degradation. To address this issue, we present a
two-stream sparse-character-based network~(TSNet) for pedestrian trajectory
prediction. Specifically, TSNet learns the negative-removed characters in the
sparse character representation stream to improve the trajectory embedding
obtained in the trajectory representation stream. Moreover, to model the
negative-removed characters, we propose a novel sparse character graph,
including the sparse category and sparse temporal character graphs, to learn
the different effects of various characters in category and temporal
dimensions, respectively. Extensive experiments on two first-person view
datasets, PIE and JAAD, show that our method outperforms existing
state-of-the-art methods. In addition, ablation studies demonstrate different
effects of various characters and prove that TSNet outperforms approaches
without eliminating negative characters.