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 …

Human trajectory prediction is challenging and critical in various applications (eg,
autonomous vehicles and social robots). Because of the continuity and foresight of the …

The analysis of crowded scenes is one of the most challenging scenarios in visual
surveillance, and a variety of factors need to be taken into account, such as the structure of …

Humans navigate complex crowded environments based on social conventions: they
respect personal space, yielding right-of-way and avoid collisions. In our work, we propose a …

Pedestrian trajectory prediction is a challenging task because of the complexity of real-world
human social behaviors and uncertainty of the future motion. For the first issue, existing …

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 …

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

Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with
specific challenges of capturing inter-sequence dependencies (social interactions) and …

Pedestrian trajectory prediction is an essential and challenging task for a variety of real-life
applications such as autonomous driving and robotic motion planning. Besides generating a …

Predicting the trajectories of pedestrians in crowded conditions is an important task for
applications like autonomous navigation systems. Previous studies have tackled this …