Pedestrian historical trajectory completion significantly bolsters the predictive accuracy of models. However, traditional statistical models such as Hidden Markov Models (HMM), which focus solely on individual pedestrian trajectories, often fall short in terms of generalization. Conversely, data-driven deep learning approaches demand extensive and meticulous data annotation as well as large datasets. Additionally, leveraging sequential historical data and uncovering the correlation between neighboring pedestrians during absences presents a significant challenge. To address these issues, we introduce a novel trajectory completion method that harnesses prior-posterior knowledge and interactive information, termed CMPT. Our approach commences with the design of a Neighbor Pedestrian Selection module (NPS), adept at identifying neighboring pedestrians through a composite scoring system that evaluates feature similarity and proximity. Subsequently, we employ a Top-Graph Attention Network (T-GAT) to extract multiple correlation sets between preceding and succeeding moments within the scenario. These correlations are then fed into the Markov-Inverse Recovery module (MR), which utilizes prior and posterior insights to flesh out the neighbor influence at the unobserved intervals. Culminating in the Trajectory Reconstruction module (TR), we integrate the completed neighbor influence data with the historical trajectory of the missing pedestrian to finalize the missing trajectory reconstruction. Empirical evidence from our experiments indicates that the Final Distance Error (FDE) of the trajectories completed by CMPT is a commendable 0.30. The source code for CMPT is available from https://github.com/ZYueliang/CMPT-Net.


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    Title :

    A Completing Missing Pedestrian Trajectories Method Driven by Prior-Posterior Knowledge and Interactive Information


    Contributors:
    Duan, Mingxing (author) / Zheng, Xinyue (author) / Pi, Huilong (author) / Ding, Yan (author) / Tang, Zhuo (author)


    Publication date :

    2025-06-01


    Size :

    1523445 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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