View Vertically: A Hierarchical Network for Trajectory Prediction via Fourier Spectrums
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Abstract (arXiv)
Understanding and forecasting future trajectories of agents are critical for behavior analysis, robot navigation, autonomous cars, and other related applications. Previous methods mostly treat trajectory prediction as time sequence generation. Different from them, this work studies agents' trajectories in a "vertical" view, i.e., modeling and forecasting trajectories from the spectral domain. Different frequency bands in the trajectory spectrums could hierarchically reflect agents' motion preferences at different scales. The low-frequency and high-frequency portions could represent their coarse motion trends and fine motion variations, respectively. Accordingly, we propose a hierarchical network V2-Net, which contains two sub-networks, to hierarchically model and predict agents' trajectories with trajectory spectrums. The coarse-level keypoints estimation sub-network first predicts the "minimal" spectrums of agents' trajectories on several "key" frequency portions. Then the fine-level spectrum interpolation sub-network interpolates the spectrums to reconstruct the final predictions. Experimental results display the competitiveness and superiority of V2-Net on both ETH-UCY benchmark and the Stanford Drone Dataset.
Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 38 of its 63 references that are in the map. First posted on arXiv 14 Oct 2021.
Builds on
38 of 63 references.
References · 38
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Citing papers per year: 2022: 4; 2023: 11; 2024: 12; 2025: 11; 2026: 6.
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