Another Vertical View: A Hierarchical Network for Heterogeneous Trajectory Prediction via Spectrums
Beihao Xia, Conghao Wong, Duanquan Xu, Qinmu Peng, Xinge You

TL;DR
This paper introduces a hierarchical network that uses spectrums and Haar transforms to predict heterogeneous trajectories, effectively modeling complex dimension-wise interactions across various trajectory forms.
Contribution
It extends trajectory prediction to heterogeneous data by incorporating spectrum analysis and a bilinear fusion approach, addressing the limitations of prior methods focused on specific trajectory types.
Findings
Outperforms state-of-the-art methods on multiple datasets
Effective modeling of heterogeneous trajectories including coordinates, bounding boxes, and skeletons
Captures complex dimension-wise interactions using spectrum-based hierarchical modeling
Abstract
With the fast development of AI-related techniques, the applications of trajectory prediction are no longer limited to easier scenes and trajectories. More and more trajectories with different forms, such as coordinates, bounding boxes, and even high-dimensional human skeletons, need to be analyzed and forecasted. Among these heterogeneous trajectories, interactions between different elements within a frame of trajectory, which we call ``Dimension-wise Interactions'', would be more complex and challenging. However, most previous approaches focus mainly on a specific form of trajectories, and potential dimension-wise interactions are less concerned. In this work, we expand the trajectory prediction task by introducing the trajectory dimensionality , thus extending its application scenarios to heterogeneous trajectories. We first introduce the Haar transform as an alternative to…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Human Mobility and Location-Based Analysis · Anomaly Detection Techniques and Applications
