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Published in:
2018 | OriginalPaper | Chapter
Trajectory Forecasting of Entities Using Advanced Deep Learning Techniques
Authors : K. H. Apoorva, Raghu Dhanya, Anil Kumar Anjana, S. Natarajan
Published in: Advanced Computational and Communication Paradigms
Publisher: Springer Singapore
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Abstract
Recent growth in depth camera technology has significantly enhanced human motion tracking. Human future behaviour and intention forecasting become a challenging task due to high-dimensional interactions with the physical world. Prognostic methods that estimate ambiguity are, therefore, critical for supporting appropriate robotic responses to the numerous ambiguities posed within the human–robot interaction environment. Beyond autonomous agents, we will also see our surroundings—buildings, cities—becoming equipped with ambient intelligence which can sense and respond to human behaviour. In this paper, we present different deep learning models that can forecast the navigational behaviour of multiple classes (i.e. pedestrian, car, cycle) by considering influencing factors such as the neighbouring dynamic subjects and social behaviour of the classes under investigation. The results show that our approaches outperform the existing state-of-the-art forecasting models.