Trajectory prediction is an important task in autonomous driving and monitoring systems. Most of the existing methods pay little attention to the rapidly changing trajectory information, but how to effectively solve this problem is crucial to ensure pedestrian safety. The Gabor transform has inherent advantages for capturing instantaneously changing information. Therefore, for the first time, we introduce the Gabor transformation idea into pedestrian trajectory prediction and propose the Multi-scale Learnable Gabor Transform Network (MlgtNet), which establishes global and local contextual relationships from multi-dimensional and multi-scale perspectives. The network first uses the Multi-scale Feature Dimension Enhancement Module (MFDEM) ascending dimension trajectory sequence, and uses the Multi-scale Gabor Convolution Module (MGCM) to guide the model to establish the dependence of different distances from different dimensions to model the interrelationship between global/local features at different scales and different step sizes. Finally, the Feature Fusion Module (FFM) processes the multimodal information and fuses it with the multi-scale trajectory features to obtain the trajectory prediction representation in different visual fields. The representation results are then used for secondary fusion to obtain the global prediction results. Experimental results show that MlgtNet achieves state-of-the-art performance with its lightweight model size on the vast majority of widely used trajectory prediction datasets from different perspectives.


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    Title :

    Multi-Scale Learnable Gabor Transform for Pedestrian Trajectory Prediction From Different Perspectives


    Contributors:
    Feng, Ang (author) / Han, Cheng (author) / Gong, Jun (author) / Yi, Yang (author) / Qiu, Ruiqi (author) / Cheng, Yang (author)

    Published in:

    Publication date :

    2024-10-01


    Size :

    2956476 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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