We develop a novel human trajectory prediction system that incorporates the scene
information (Scene-LSTM) as well as individual pedestrian movement (Pedestrian-LSTM) …

We develop a human movement trajectory prediction system that incorporates the scene
information (Scene-LSTM) as well as human movement trajectories (Pedestrian movement …

Pedestrians have an intuitive ability for navigation to avoid obstacles and nearby
pedestrians. If we want to predict future positions of a pedestrian, we should know how the …

Human motion and behaviour in crowded spaces is influenced by several factors, such as
the dynamics of other moving agents in the scene, as well as the static elements that might …

Recurrent neural networks have shown good abilities in learning the spatio-temporal
dependencies of moving agents in crowded scenes. Recently, they have been adopted to …

Mimicking human ability to forecast future positions or interpret complex interactions in
urban scenarios, such as streets, shopping malls or squares, is essential to develop socially …

Accurate predictions of future pedestrian trajectory could prevent a considerable number of
traffic injuries and improve pedestrian safety. It involves multiple sources of information and …

Predicting trajectories of pedestrians is quintessential for autonomous robots which share
the same environment with humans. In order to effectively and safely interact with humans …

In this paper, we propose a human trajectory prediction model that combines a Long Short-
Term Memory (LSTM) network with an attention mechanism. To do that, we use attention …

Pedestrian trajectory prediction is fundamental to a wide range of scientific research work
and industrial applications. Most of the current advanced trajectory prediction methods …