Particle-based pedestrian path prediction using LSTM-MDL models
This work addresses path prediction for pedestrians in security applications, but it is incremental as it combines existing techniques like particle filters and LSTMs.
The paper tackled pedestrian path prediction for security risk assessment by proposing a combination of particle filter sampling and an LSTM-MDL model to handle multi-modal predictions, finding that the simplest approach performed best in synthetic tests and demonstrating feasibility on real-world scenes.
Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf 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 the path of pedestrians for risk assessment, a point-wise greedy (ML) 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 filter sampling strategies and a LSTM-MDL model is proposed to address a multi-modal 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.