SpectrumNet: Spectrum-Based Trajectory Encode Neural Network for Pedestrian Trajectory Prediction

Abstract

Extracting motion pattern implied in the history trajectory is important for the pedestrian trajectory prediction task. The motion pattern determines how a pedestrian moves, including but not limited to reaction of interaction, tendency of speed and direction change. Although the motion pattern is a comprehensive concept and can't be described concretely, it is clear that it contains both long-term and short-term factors. Inspired by this, we introduce SpectrumNet which enables more effective encoding of historical motion patterns for trajectory prediction. Different from existing methods, which consider the history trajectory as a time sequence of position, SpectrumNet represents it in the frequency space by applying Fourier Transform (FT) to decompose the historical information on different time scales. SpectrumNet consists of two sub-networks, the Multi-Frequency Combination (MFC) encoder, which models the historical information by combining multiple frequency feature in the spectrum; and the Frequency Interaction (FI) encoder, which captures the interaction between pedestrians in the frequency domain. To validate the effect of SpectrumNet, we build a CVAE-based prediction system to predict stochastic future trajectory. Experiments conducted on ETH-UCY dataset show that our prediction system with SpectrumNet out-performs the previous state-of-the-art model and achieves a new record on ADE metric.