Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …

The Internet of Things (IoT) has created a ubiquitously connected world powered by a
multitude of wired and wireless sensors generating a variety of heterogeneous data over …

Predicting accurate future trajectories of multiple agents is essential for autonomous systems
but is challenging due to the complex interaction between agents and the uncertainty in …

Human behavior has the nature of indeterminacy, which requires the pedestrian trajectory
prediction system to model the multi-modality of future motion states. Unlike existing …

Better machine understanding of pedestrian behaviors enables faster progress in modeling
interactions between agents such as autonomous vehicles and humans. Pedestrian …

Understanding crowd motion dynamics is critical to real-world applications, eg, surveillance
systems and autonomous driving. This is challenging because it requires effectively …

With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …

Most recent successes on forecasting the people motion are based on LSTM models and all
most recent progress has been achieved by modelling the social interaction among people …

Since the past few decades, human trajectory forecasting has been a field of active research
owing to its numerous real-world applications: evacuation situation analysis, deployment of …

The maritime Internet of Things (IoT) has recently emerged as a revolutionary
communication paradigm where a large number of moving vessels are closely …