Particle-based Pedestrian Path Prediction using LSTM-MDL Models

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.