Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a probability density function over the state space. Therefore, recurrent models are a natural choice to address path prediction tasks, where a trained model is used to generate future expectations from past observations. When applied to security applications, like predicting pedestrian paths for risk assessment, a point-wise greedy evaluation of the output pdf is not feasible, since the environment often allows multiple choices. Therefore, a robust risk assessment has to take all options into account, even if they are overall not very likely. Towards this end, a combination of particle filtering strategies and a LSTM-MDL model is proposed to address a multimodal path prediction task. The capabilities and viability of the proposed approach are evaluated on several synthetic test conditions, yielding the counter-intuitive result that the simplest approach performs best. Further, the feasibility of the proposed approach is illustrated on several real world scenes.


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    Title :

    Particle-based Pedestrian Path Prediction using LSTM-MDL Models


    Contributors:


    Publication date :

    2018-11-01


    Size :

    1166635 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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