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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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SMEMO: Social Memory for Trajectory Forecasting.

Francesco Marchetti, Federico Becattini, Lorenzo Seidenari

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    This study introduces a novel neural network with working memory for modeling human interactions and predicting future trajectories. The method effectively learns causal relationships between agent motions, achieving state-of-the-art results in trajectory forecasting.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Robotics

    Background:

    • Modeling human interactions is crucial for predicting future behaviors, especially in trajectory forecasting.
    • Individual movements influence surrounding agents due to social rules like collision avoidance and group following.

    Purpose of the Study:

    • To develop an algorithmic approach for modeling time-evolving human interactions.
    • To present a neural network with a working memory for enhanced data manipulation in interaction modeling.

    Main Methods:

    • Utilized a neural network with an end-to-end trainable working memory for storing, updating, and recalling agent information.
    • Treated interaction modeling as a data manipulation task.

    Main Results:

    • The proposed method demonstrated the ability to learn explainable cause-effect relationships between agent motions.
    • Achieved state-of-the-art performance on multiple trajectory forecasting datasets.

    Conclusions:

    • The neural network with working memory provides an effective framework for modeling complex human interactions.
    • This approach advances the field of trajectory forecasting by capturing dynamic agent relationships.