Constrained Gaussian Processes with Integrated Kernels for Long-Horizon Prediction of Dense Pedestrian Crowd Flows

In this paper, we present a novel approach for predicting pedestrian crowd dynamics over longer time hori- zons (30s). In dense environments over long time horizons, the number of pedestrian interactions is high, leading to the degra- dation of traditional pedestrian trajectory estimation techniques. Alternatively, we consider the macroscopic properties of the crowd as a whole, focusing on the flow of density. This approach benefits from not considering pedestrians individually, and can probabilistically estimate the existence of previously unobserved individuals. We propose a novel approach to imposing a physical constraint on the crowd density flow. Initially, a coarse resolution prediction is generated by a Convolutional Recurrent Neural Network (ConvRNN), and subsequently smoothly interpolated by a Gaussian Process (GP). Using the linearity properties of GPs, a continuous representation of the crowd is produced that complies with both the ConvRNN’s prediction and a conservation of density constraint. The approach is trained and analysed on the dense ATC dataset, where we show the advantages of the approach and the improvements from our contributions.