Attention-Enhanced Pedestrian Trajectory Prediction via Compressed Point Cloud Representation

To address the high storage overhead and inadequate spatial geometric representation associated with raw point cloud data in multi-pedestrian trajectory prediction, a compressed point cloud-based and attention-enhanced trajectory prediction method (CPCAE) is proposed in the paper. First, for input raw point cloud, a lossy compression module is designed, which improves the Depoco framework by introducing a multi-feature extraction component and employing a coordinate decomposition strategy to optimize compression quality and spatial representation. For input video frames of pedestrians, spatial features are extracted using a 2D convolutional network, and dynamic interactions among pedestrians are captured by a Transformer-based encoder. Then, both spatial attention and modal attention mechanisms are incorporated to dynamically balance the contributions of two modal features and precisely identify key regions and positions. Experimental results evaluate the proposed framework from the perspectives of point cloud compression and downstream trajectory prediction. The results demonstrate that compressed point cloud representations can support competitive trajectory prediction performance in CPCAE.